{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb991ee1173fd08",
   "metadata": {},
   "source": [
    "# GoogLeNet 识别手写数字\n",
    "2014年，获得ImageNet图像分类竞赛的冠军是GoogLeNet，其解决了一个重要问题：滤波器超参数选择困难，如何能够自动找到最佳的情况。 GoogLeNet 并没有打错，其中的 L 大写是为了致敬 LeNet 。\n",
    "\n",
    "GoogLeNet 在网络中引入了一个小网络——Inception 块，Inception 不能被直接翻译，不过这个单词有“开始”“植入”的意思，《盗梦空间》英文名就是这个。\n",
    "\n",
    "Inception 的引入像是给每一层的神经元提供了不用的处理方法和路线，最后再进行汇总。谷歌发布的论文 [Going deeper with convolutions](https://arxiv.org/abs/1409.4842) 有介绍。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7e7c3d9023d48984",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-07T07:07:24.223460Z",
     "start_time": "2025-08-07T07:07:24.197790Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FyzcLxGPnDmhz/ywQt8TL7YebNpSdVfNmVfoPejooKEh2XOvUe7DPM71kxcjixSz70+XZR8g2fLh+7349DVmRfbhAzk9D9qJV2Vq8RHHZ6x445NlpsZNcHvSnefzx7Qj9KQBYoj+F49i6d+iTqP5U9h0bPdJAn5ZtmmnLWPan2rra9/c/2LBc7vZ7rq+2Ysrp5GKRxWR1dVc2KwvMWTRTWyAwQn8KAACczNY9ZHUy0I8/+0A/8fUls9dsWvXhxvdl1rTYSfpZazevlolTZ2dNzLlA7NStY6HQQuoUpSqnLx+fPjdm58Gti+Pmqd3aC1f//Fr9pV/OhYWFWfanm3etl9svj43WtiPJ+OmsrKJu5/w0+vTvLbfnL33tm1/Pq7kJ2z9yedCf5vHHtyP0pwBgif4UjmPr3qFPojrQ4lGRsh+mT526tbRlPOxP+zzTS+7uPfaFfvvd+3QNCgpKu5hyObs/DQ4OPvPtCf0CARD6UwAA4GS27iHvOpQoe5jPvtBfm3I4fZ/sUg4eNlD2KiOKRNSsdZ/WVEq69e4iy39xIOv7TzkXiLELp8tt7SqpkiXvLZQpK9a+PWTEC3Jja9JGbdbq9VkHCrRo3VTdnThtrNxNTs36kb/+4bTsKleqXFErWyXDxgwpFFro0x0fX87tadRtUKdI0SLaLMmQkVnLj48Zre62atv87orltblaf5rHH9+O0J8CgCX6UziOrXuHPskdf39fW1frTxs+XF/uVq56jz7FoyJloroaaaNHGpQqXdK8/YIe+lMAAOBkdu8hd+3VOTg4eOjolxL3bvpo8+o69R6MLF5s34ldMmvC1DGu7NNJfbJ1jexwDhj8jNzt/mQXtWLOBeK5K2lVqlWWTc19Y9bOg1vfXL5Q9lRl41//cPrtVUtksU7dOu5P+1JWX/j2XNkZDgsLkz1htZ3YRTNkgT79e6v6Vd1t3KThmk2rZFOTZ4wPCQl+uGlDdUhpzk9DPeeJ08YeO38wOXX3yAnDIiIKu26ew1TSpecTsjX5SdV1n7T+9HLefnw7Qn8KAJboT+E4du8dej/52J/WbVAnNCx0+Lih5qgvNzV6pMFdd5czb7+gh/4UAAA4md17yBeunuo/sJ/sZ7qy3VfzXnVcp0rMrFeKlyiuZhUpWmTIiBe0r+TnWiAePJXUonVTta5o/3jblNNZ10SVDB42MKRQiJpeueo973zw5lPP9pHncDLjiMw9enZ/9RpVXdlnwVLLv7Z4lrrGlAgODu7ep+upS8c8eRqnLh1t1ba5WlE80qzRjv1bqlSrXKfeg2rhjzavVkckdO3V+fKt/enlvP34+R76UwCwRH8Kx7F779D7ycf+9PHOjxp20SSyi3n++z+/ykR/CgAAEHi8s4d89krqtn2b9VeT15Lx09mdB7dKtN3O28qx8wdly6cuHTVMP/PtiR37t6gv6Zvzza/nZb/33JU0bcqlX859mbL9iwNb5Kmal8858hwS926S/5pnSS7+eObo2f3yX/Osy3n+8fMx9KcAYIn+FI7jnb1DbyYv/enWpI0u3bntX18y23XrufO//uF09RpVI4sXO/fdycv0pwAAAIEo8PaQyZ2F/hQALNGfwnECb+8wL/3pgZN7XNlfoZoya2Lq14e/+iH9wToPBAcHvzR88OZd69cnrm3VroUsMHnGeLU8/SkAAEDgCbw9ZHJnoT8FAEv0p3CcwNs7zEt/KunRp5s63dKqT96Tu2kXU7r26qydK0rWmjp7krYw/SkAAEDgCbw9ZHJnoT8FAEv0p3Ac9g7NOXXpWOrXh/VTTl8+vm3f5n0ndmX8dNa8fOCF/hQAADgZe8hEhf4UACzRn8Jx2Dsk5tCfAgAAJ2MPmajQnwKAJfpTOA57h8Qc+lMAAOBk7CETFfpTALBEfwrHYe+QmEN/CgAAnIw9ZKJCfwoAluhP4TjsHRJz6E8BAICTsYdMVOhPAcAS/Skch71DYg79KQAAcDL2kIkK/SkAWKI/heOwd0jMoT8FAABOxh4yUaE/BQBL9KdwHPYOiTn0pwAAwMnYQyYq9KcAYIn+FI7j5L3DjJ/Pnf/+lHm6TTnz7Qnt9sx504aPG2pexk9CfwoAAJzMyXvIRB/6UwCwRH8Kx3Hs3uHOg1tr1a655csE8yw7Ehf/1j1VKml3mzRvXKJkCfNifhL6UwAA4GSO3UMmhtCfAoAl+lM4jmP3DqfMmuhyubzWn7Z7rE1EkQjt7ocb339n9RLzYn4S+lMAAOBkjt1DJobQnwKAJfpTOI439w4zfjprnqjyza/nL/54xjxdn5wXyPj5nHnin7N+Omuem0N/+vUPp80T9cn5mcjq8uMYJhr6U3fJ40PnV+hPAQCAk3lzD5n4c+hPAcAS/Skcxwt7hxk/n5s8c0KVapWDg4OLR0V27tHpwMk92txNOz9p2aZZWFiYy+WqeE+FcVNGaTXrli8TatWuuT5x7aChzxcvUVwWqFS5YuzC6fqN79i/5dFO7SOKRISEBFevUXXuG7O0WUfP7u8/6Om77i4nKwYFBckTmDp7kpr10vDB5cpnTa92b5U2HVqpiSczjsjyJUuVlOlRJaOeGfiUTNG2Js8kdtEMiTxJWUCez8Ahz164+u/Tp8qzfeyJDpHFi8nckEIh9RvWXb1+uZrVpMXDxSKLyY8vGxk5YZhM6TvgSZmorZtyOrnXUz3kl6Me+qln+xy/cMjzh8730J8CAAAn88IeMikQoT8FAEv0p3AcL+wddu7RyeVydejU/vUls8dOHlW0WNFKlSueunT0cvbX2EMKhZQpW3p8zOi5b8xq91gbWfLRTu3V8ZvrtsTL3cpV76lSrfLYSSNlmfIV7pIp8Z+uVFve8mVCREThEiVLyGbnL32tReumMvfVOZNl1rnvTt5TpVKRokWGjxu65N0FMbNekY3I3Hc+eFPmvrdmWdtHW8vd517srwrZ05eP31fzXnkyz77Qf8GyuS+8PDA0LFSmnL2Sqh5LPZPI4sVk1tTZk+o3rCtTho0ZouZu27c5PDxcHlFmvfHu/JfHRheLLCZbOHImWeZOnxtz733V5O4rr457f927l289/+nh9H133V2uUGihQUOfl59iwOBnQkKC5VeUdjHFk4e2I/SnAADAybywh0wKROhPAcAS/Skcx+69w7WbV7tcrgGDn9GmvL1qiUyZ/tqUS7+cq1S5YpGiRQ6n79Pm9unfW+bKMpdv9qfVa1Q9//2fx1pu27dZpvTo003dbdykoay+P+1LdTfjp7O1atcsVbqk3Ji/9DVZcv5bc7QtJ6fulilde3VWdw3f348e9aLcXfLeQm35FR+9I1NGvzJc3ZXbhUILJR39Qt29cPVUmbKly1e4S93t9VSP4ODgvcf+nCuRh5ZV5iyaqe4avr+v70979+spSy5f+7Y2d9E7r+t/aTk/tB2hPwUAAE5m9x4yKSihPwUAS/SncBy79w6fe7G/y+Xae3ynNiXj53PrE9eezDiyNWmjK/sIUP3yh9L3ysQuPZ+4fLM/HTE+6wvvWsLCwlq1bS43Tnx1OCgoqHufrvq5SUe/2H1426VfzsmjHDi5R3860WPnD4aEBHfo2E7dNfSnlSpXLFe+nH5TkrvuLlez1n3qtizcuElD/dxW7VqEhoWq21/9kK7VuCrvrM6qibUzBrjrT+WpFo+KvP+BGvp1v/n1fOWq95QpW1rdzfmh7Qj9KQAAcDK795BJQQn9KQBYoj+F49i9d9imQ6uQQiHm6ZdvHog6e8GrhulFixWt26DO5Zv9qWGByOLFmrdqIjc271ovc8dNGWVYXcvFH8+8uXzhcy/2b92+pTpzqGj3WBs1V9+fypJyOzw8vHLVe/QJCwuLiCislpcFnujeUb/9Rzu1DwkJ1u6mXUyZPjcm68SmzRurk6gKeRQ1111/euKrw+YtSzp0bCfTz3138rIHD53voT8FAABOZvceMikooT8FAEv0p3Acu/cOm7Vq4u5IyTeXL3S5XK8vmW2YXjwqskGjupdv9qexi2bo52r96frEtTJ34rSxhtVVjl84VL1GVVmgavUqT3TvOG7KqA3b1gYHB1v2p+e+O6mWHD5uqCHa0a+um0fFatGXmLLxIkWLyN06dWs9+XTP6XNj1Pf3c+1Pj57dL4sZjqKVPPZEB5n+1Q/pl3N7aDtCfwoAAJzM7j3kgMmRM8kbPv/4VEbWhQ0CMvSnAGCJ/hSOY/feYY8+3Vwu17HzB/UTR78y/J0P3tywLasAHTr6Jf2sU5eOysTHOz96Obf+dN+JXTK3/8B++rmfbF0zdvKoY+cO9Hqqh8x949352qzTl4+73Bx/Kileonj1GlX1m7qc/ZX/jJ/Oqts5l5gVKt1dLLLYrkOJ2twVa9/2pD+9+OOZ8PBw1RfrU7PWffKU1O2cH9qO0J8CAAAns3sPuUDnVMbRz3dtXPbeWxMnTYjOtj3pM/NigRH6UwCwRH8Kx7F773Bx3DxX9tWitCmJezfJlOHjhn71Q3qJkiXK3lVWfUtdZdyUUTJ37huzLufWn0ruqVKpXPly+tU7dGofHh5+9kpqo0caBAcHX/zxjDZLHRDaql0LdXfq7Elyd9POT9Td7n26yt0PN76vLb/vxK5CoYVatG6q7uZQYn79w+mgoCDDKUp79u0uq7zy6jh1t0PHdvLEtLn660e1e6yNrC6/Fm2uapa7P9lF3c3hoW0K/SkAAHAyu/eQC1wufH9q54HEFfHvjp84XnWmenGr/n0d1ABLNP0pAFihP4Xj2L13+PUPp2vVrhkaFjpx2titSRuXr327SrXKpcuUSjmdLHNjF81wuVx1G9RZ9cl7W75MGDlhWEhI8IN1HlDfW8+1P126crEs0KBR3fiEFbLxF4cPkrsvj42WWS8NHyy3Bw55Njl1995jX0yaPj4ionBYWFjDh+urdRcsm+vKPhw1duF0ubv3+M5ikcWKR0XOmj9te/Jn761ZVrV6FXna6xPXquVzLjEfql87pFDI/LfmHE7fl7h3U98BT8rDaU9G0rtfT7k7KPq5FWuz9i/1/aksL0+sTNnSC9+eK7fnvRkbVTKqaLGi2kW3cn5oO0J/CgAAnEztIQPR9KcAYIX+FI5jd396OfsSSY93fjQ4ONiVrW6DOvpjLee9GVumXBk1KzQstNdTPU5d+vMMSrn2p5Il7y4oe1dZtXrhwuEjJwy79Ms5mX72Sqo6hahy/wM1Ptz4frfeXeQhTmYckQXkv7Vq15RZQUFB566kyZTtyZ81bFxPW+Xe+6rFJ6zQHsiVY4m58+DWBx68X60oG2zdvuXe4zvLlS9Xp96DaoFNOz8pVTrrolLq0lj6/lTy6Y6P1ZNRHmnWSDbo4UPbEfpTAADgZOnp6YsD16xZs4wdoZURI0bMnj17+PDhxhlWxmSbOHFiTDZ5iNhsxscugNglBgAz+lM4zmL7+1OVkxlHtu3bbDgRqsqlX87tObJjx/4tZ749YZ6ba9Tquw4lfv3DacOstIsp8qCH0vea11KR53P68vFbppw7sDVpY8rp5G9+PW9ePofI8urgU9XPmpPx09mjZ/erQ2sto1Y/fuGQeZaXQ38KAAAQwEaPHm1sQHVGjhw5atSoxTePu0xOTvawRXVnzJgxqouMj49PzJaR7erVq7c+LwBAwUB/CsfxWn9KClDoTwEAAAJYfHy8seO8afz4P89wqt8VvH79+rp1625d0IKsO336dPl8sXDhwgULFqgPGp6IiYlRBWtCQgIFKwD4P/pTOA79KTGH/hQAACAgXb16NSEhYcKECcYKM9u4ceO02xkZGYZ1ZUpsbKxu8Szjx4+XDVpWpTExMXFxcbJLefbs2TNnzsjqSUlJqh5Vbal5a+5opwJQq8t2VMGamZlpeJIAAC+gP4Xj0J8Sc+hPAQAAAklmZmZCQkJMTIyxmNQxfEnfuImbkpKS9F//f+edd7RZV69eTUlJyblOVdWnbntZ5OmpPlQVrGoLIucnrGcoWOVpqA1ev37d8FgAgLyjP4Xj0J8Sc+hPAQAAAkBmZmZSUpL+MM8xY8bEx8enp6cnJCRoE19++WXttrI4x4vOX79+XTsDwMGDB42zb1J9qCxpeZypTJRZlnWq2dWrV1UfqurROyhY1fLqeFh9wWp8JACAB+hP4Tj0p8Qc+lMAAICCK4faVFvm6tWrataIESO0xTQJCQm67VnLyP46v3Gqe/lYp5qpMlR+QFWPyqZUYSo/uPGRrJivcCWbomAFAHfoT+E49KfEHPpTAACAAsdcm4q4uLiUlBTjotnGjx9vPvJU0TetNrG1TjVTZaj8KlQ9Kr8WVZgaH9gNrWDlClcA8H/0p3Ag+lNiDv0pAABAQXH9+vWUlJS4uDh936dq05zP/pmUlDR58mT9WpqcV7SDl+tUA/l5LQtWyydjKSYmRl+wcoUrAIGN/hSOQ39KzKE/BQAA8HN3XJvqyZL6E6Eqt/WtfJv4tk41M1zhSqi21PLpWYq99QpXFKwACjT6UzgO/Skxh/4UAADAP1nWprGxsUlJSZ7XpnoZGRn6TUV7dvJTL/OkTpVfiw+/Ta8VrKoezccrXN3ZnxUAbEV/CsehPyXm0J8CAAD4m/T09Pj4eP0FkVRtmpcDGK9fv64KvnXr1mkHonrh5Kd5ob5rn5j9FXvLdnJx9pfofVunmsmT0Rest3uFq2hTwcoVrgD4EP0pHIf+lJhDfwoAAOAn7KhNNaozjYmJUQc5ZmRkaLcLigJap5qpMlT+3HdWsGpXuJIV9QWrn//UAAoo+lM4Dv0pMYf+FAAAwLfMtWlMTExCQkK+1KZKxs1v7gfSMYwBU6cauLvClfo05wnDFa6E2mDB+j0A8BP0p3Ac+lNiDv0pAACAT1y9ejUhIUFf/KnaNN9LLu2b+354ttN8FKh1qplWsN7xFa60glWtzhWuAOSA/hSOQ39KzKE/BQAA8Cav1aYawzf3HcI5daqZdoUrQ8Fq+XuwFBsbqy9YucIV4GT0p3Ac+lNiDv0pAACAF5hr0zFjxsTHx9td3mUE4jf374AndWpi9olEnXAMpuEKVzIyb7dgVctrV7iiYAUCGP0pHIf+lJhDfwoAAGCfzMzMpKQk/RerVW3qnQvfO+Sb+3dA/i7q8k2Lra7apC7Q5Jw61UwVrPl+hSuHl/hAAUV/CsehPyXm0J8CAADkO9/WphpnfnP/DlCn3i5VhmoF6+1e4cpcsKoN2n04NoA7QH8Kx6E/JebQnwIAAOQXc20q4uLivFybKhl8c/9OUafmkXaFq5SUlDsrWLUrXCUkJOgLVn7hgPfRn8Jx6E+JOfSnAAAAeXT9+vWUlBRDNxQXFycTfXXgJ9/cz0fUqfnO3RWuDP/2kAPDFa5kOxSsgE3oT+E49KfEHPpTAACAO6Nq07i4OH2t49vaVMM39+1DneoFWsGq6tE7uMKVKli5whWQd/SncBz6U2IO/SkAAMBtsaxNY2Nj/aE2VTL45r4X6etU/ZBQxowZoyo8mrv8pa5wZS5YzY22O2p5c8FqfCTA8ehP4Tj0px7mwvendh5IXBH/7qszpspvzLxAIIX+FAAAwEPp6enx8fH6CiY2NjYpKcmvDjPkm/u+dfXq1ZSUFFXn6YeKIn8a6lTvUGWodoUr+T/3tgpW8xWuZFMUrHAm+lM4jhP60893bVwR/+6aDavlx7ytn9TQmWpGjBxx5EyyefmACf0pAABAzlRtqq9d/LA21fDNfb9Cneq3VBma71e4kr+48ZGAAo7+FI6j3gxenTF1zrzZgZpXZ97Sfophw4aNGj1q7PixEydNmDJtsiwwe+5MtfDMOTMmTXll9JhRsoxhLT1t+YCM/Fqi6U8BAABMzLVpTEyM7DX5Z22qZPDNff9GnVpQyO/fULCqttTzK1wZClaucIWCi/4UjmP4thGgkd0C43ABAABwpKtXryYkJBhqU5ni/4eV8c39Aoc6teDSrnCVlJR0ZwWrusLV4uyrjekLVv7W8Df0p3Ai9Yoc2KZONR6Cqjdy5MhPPvkkNTV14sSJZ8+eXbRokXGJHMla8j63cOHC+GxJ2WQ7xidRoPj/hwEAAAC7qdpUf3XvglKbavjmfkFHnRpI3F3hSv8ikzO1vPkKV/z14WX0p0DgyMj+d7/4+Pic343kvUd9XUIWlrvp6en/l/3GNm3aNOOiOqOyGaeaqC9oaG9vvLcBAAD4P3NtOmbMmIJVmyoZfHM/4Gh1quUhjTJRHc/BX7yAUgXrHV/hKvpmwcoVrmA3+lOgAMvMzJS3B3f/Nms2evRo1ZYqahdZ3mm0KbLnMXLkSONqJqNGjVLfs1i5cuWyZcsWe/YFDe3cN/o3Nk58AwAA4CuyJyZ7ZYbaVHYO9XuMBQjf3HcC7ZARyw8g1KmBR5Wh5oLV+Ld3Q17TLAvWAvePQ/A5+lOggMnI/u5DXFyc+V/kZIr2ZZZLly4Z5sp+pP44UHnD0Gbpp8tutPnd6MyZM+odyzwrOrsY1XZTFHX6G/XeZrlnY6C9q+nf0qhWAQAA7CB7WbK3pt9JK9C1qYZv7juQ+uhBnepY5itcySfi2ypYtaN8ErKvcKU+TWdQsMKE/hTwd+obK+72CdQLvSxgbhu1ZWRF8x6D2r9UzNdNkr3n0aNHawsYNu7JU5ItGNaiWgUAAPAhc20anf1VpIJemyoZfHMf1Kkw0QpW9Tk0Mf+ucMVnUqehPwX8jrzEawd7mg8yVWdM9+Rdf3H2v7nJdowzsunrUVnSODv7aaiOddWqVcZ5OuoNKdGDQ2KNa+ro39K0c4obNmVJLan+qVCdSpx/JwQAANCTvTXZTTKUBbKHJhMD5jhNvrkPS+ojBnUqcpCZmWn5aTTna4roGQpWrnAVqOhPAb9w9epV9dZu+TKtXovT09Nv6yU4w/2/icmmDA/hbsnbbSRlOzlcLlPeWtTRsp5vU51QXH0dg2oVAADAQ6o2jYuL0+8gBVhtquGb+/AEdSrugPpAmpF9zNCdFaxqee0ay1rBanwk+Df6U8A3cr70k3rzvq2e8bbIxg2PKDsKxoXyg9pHkbcK87uL+mJ+YvahqXe2p5uP1SrvXgAAIDBY1qay5xOQtamSwTf3cUdy+KgSrTtPmk2fyBAw1MdJ8xWuzN/OtOTuCle8oPkb+lPAezJufs/d/A6t/5673bu2sn3Do0dn/3O9cbn8pj8vgfHhdRehypcdFFWtau9hnr+BqS9fqLcu7dQ2xq0DAAD4B3XcJRBtz7kLZE/Yk11oOIF8UDKODzsx9qDx8thzh/4UsJG6zpLsylh+Q0T7J0133523iTyi8alky5fi0nN3dhGqvFMnuKFaBQAAAcDyn6XhTIutLmmQR7Lfa3wYOJhxfNiJsQc94/jwBfpTID9pF1OyrOQ8v/STrdztZ9vxT9YeyvkiVOr3pg7ONa6Zr/KlWlXPM8P+44gBAICTyR6L7HsMGzZM2yGJmTbrw/U79qf+kJbxH8RRSdx7JtrO/nTFh5vND0ocFfUiYxwfdlJjb+vG92/84zRxcrw/9tyhPwXyyo5LP9knMzPT+BRvGjNmjHFpH8n3i1DlnWp4Vcmret7Fnp0yXJZZrDtZONUqAADIC9mRkN1Ow7/vzlv8zu7Dl8yVB3FI6E+J3VEvNcbxYSf6U6Li/bHnDv0pcNt8e+mnPNJ6QLXzLU9Y/qtN8c9eL8P9md3zfhGqvKNaBQAAdpMdS9n51O9gyG2ZMnfuXLmduPeMue8gzgn9KbE76mXHOD7sRH9KVLw/9tyhPwU8otoxv63w7ox6T5L/Gmf4MVVeJ3rlIlR5Z1mtWp7v1UBVq2pcJd68AmO+nwoWAAD4OXe1qbaro/aI6E8dHvpTYnfU649xfNiJ/pSoeH/suUN/CljL+dJP2lfIC3SlVRD7UwPt5AmWf6bFtl2EKl+oalWev/wJ5EfwsFpVfT3VKgAAAUze2Q21qewAyN6CvO8blqQ/JWn0p8T+qBci4/iwE/0pUfH+2HOH/hT4U4G49FP+CoD+VM+Tv2Ci/RehyhdUqwAAOJC8a8u7v/5N311tqqE/JWn0p8T+qFck4/iwE/0pUfH+2HOH/hSO5uGlnwK1gQqw/tRAuwiVZfNYcI8g1ler6iS86oNTrtSSsoqsKD+4bMRPznIAAIDD3UFtqqE/JWn0p8T+qJcm4/iwE/0pUfH+2HOH/hTOkvPZM9Wln/zn7Jl2C+z+1EB1jgF2BlsDGbfyI6SkpFCtAgDg/2Svw1CbCtlXkfdiz3dI6E9JGv0psT/qBco4PuxEf0pUvD/23KE/ReBzQnF2ZxzVn+o5sEanWgUAwE/IPqe8pcquqf4N93ZrUw39KUmjPyX2R71SGceHnehPiYr3x5479KcIQIH6xe1859j+1CDXi1AlBvRpHPTVquqUF1udQNZMfl2ypPzeZC11auCMgnBuWQAAfCJ/a1MN/SlJoz8l9ke9ZBnHh53oT4mK98eeO/SnCASeXDgowC79lC/oT80YS3qZmZnyk6pjdalWAQC4M+baVN4o5f0xL7Wphv6UpNGfEvujXruM48NO9KdExftjzx36UxRUDj9mMF/Qn+aKY5ndyd9qNV8+QAIA4FfkXVLe7/Rvjqo2zd/dBvpTkkZ/SuyPehEzjg870Z8SFe+PPXfoT1FgOPCclXajP71dGZxL1wOqWs3IPoxXyK9LfjPm35iZLCNLyvJqRapVAEBB5J3aVEN/StLoT4n9Ua9mxvFhJ/pTouL9secO/Sn8GnWVrehP84JC/w6o0yNQrQIAAo+5NpU3r4SEBJtqUw39KUmjPyX2R72sGceHnehPiYr3x5479KfwLx5+XZpOKl/Qn+Yj7YQSlm2g6vo5oUQO8qtalV+ybITfMwDAO2QHQPZO9W9Yqjb12s4q/SlJoz8l9ke9vhnHh53oT4mK98eeO/Sn8DFPLteTmH24mXFN5Bn9qU08GdXOuQhVvlDVqvzS5LcaHx8vv1jLf2IxUEepq/KaahUAkI98Xptq6E9JGv0psT/qhc44PuxEf0pUvD/23KE/hQ/keukn2fvkSD0voD/1DhnwnhxVzYC/M1SrAACvMdem8oYi7z4+/DdR+lOSRn9K7I96xTOODzvRnxIV7489d+hP4Q3Xr1/P4UyRsg/KmSJ9gv7UJ9ShqZzV1wvM1arlS5CBVq3KJ2RZMSUlRTbCqxMAOFlmZqa8m+j/fU7VprJ/a1zU6+hPSRr9KbE/6qXPOD7sRH9KVLw/9tyhP4VdVG1BSeTP6E99Tl2EKiEhwbLXUxeh4oS/dpBfqbz+yO9Wxr/6/Vv+CcyoVgHAOfy5NtXQn5I0+lNif9RroHF82In+lKh4f+y5Q3+KfKNd+smyhuDST36I/tTfeHgRKv7VwVZUqwDgcObaVMTFxclru3FRP0B/StLoT4n9Ua+ExvFhJ/pTouL9secO/SnunHaRHNmhdHeRHHWQqXFN+Af6U3/GRaj8kL5aVX8ay7+OmXwIlyXj4+NlLfVX4w8HAP5G3nnlFV7eXvUv4Ko29ed/uaQ/JWn0p8T+qJdE4/iwE/0pUfH+2HOH/hS3R10Jh0s/BQb60wIk54tQqf/1uAiVD8lvPiMjQ53omWoVAAqQAlqbauhPSRr9KbE/6rXROD7sRH9KVLw/9tyhP0UuuPRTAKM/Lbi0Q7/N3/QfM2aMduh3gfjgF/Dyt1rlbwoA+cKyNpUX3oJSm2roT0ka/SmxP+pF0jg+7ER/SlS8P/bcoT+FhQwu/eQM9KeBgYtQFVyqWlVtuCrE5Y9ofuE1k2VkSVWUqxdkXpMBwEPypinvjPoXVXmvlF3fAvodDvpTkkZ/SuyPerU0jg870Z8SFe+PPXfoT5GFSz85E/1pQOIiVIFBnQCXahUA8ouqTfWH/xfo2lRDf0rS6E+J/VEvm8bxYSf6U6Li/bHnDv2pQ+V86Sf993+NayKA0J8GPP35N8z/p3MRqgIqf6vVgl4cAEDOzLWpvBjKa2DAvPrRn5I0+lNif9Trp3F82In+lKh4f+y5Q3/qIFz6CQb0p07jyYsAF6Eq0LRqNSkpSf7Xlr+1/Fkt/9wG6tws6vDkxOwjlKlWARRo8pYnb2qG2lSmBN63qehPSRr9KbE/6oXUOD7sRH9KVLw/9tyhPw1kHl76iUPPHIv+1OHUAYy5HoTOV78DBtUqgICnalP9IfmBWptq6E9JGv0psT/qFdU4PuxEf0pUvD/23KE/DTTqs3HOl37i1IdQ6E+h4SJUDpcv1aqMENkIgwSA95lrU3mBklczJ7wi0Z+SNPpTYn/US6txfNiJ/pSoeH/suUN/WuDReuCO0Z/CHVWlWV6Ein+JcRp5+5DxIO8j8kdX7zWWbzdmaklZhWoVgE1kN1jercy1qbxDGRcNXPSnJI3+lNgf9RprHB92oj8lKt4fe+7QnxZIHn7r1rgacCv6U3gi14tQcSYQx6JaBeATqjbVHybvwNpUQ39K0uhPif1RL7bG8WEn+lOi4v2x5w79acHgyVVfuPQTbhf9Ke4AL0fwhGW1au7fzdSSMsBkRdXLU80DUMy1qXBsbaqhPyVp9KfE/qiXXOP4sBP9KVHx/thzh/7UT3HAF7yA/hR5x+HwuF2ZmZkyJNR7nHqbs3ynM4uNjaVaBRxI9opTUlIMtam8v8hETiPzf/SnJDv0p8TuqNde4/iwE/0pUfH+2HOH/tSPZHDCQXgX/SnyF6djRh5RrQLQqNo0Li5O/z87takZ/SlJoz8l9ke9CBvHh53oT4mK98eeO/SnvkTXAN+iP4Wt+Dch5CNDtRoXFydDyDy0zGQZWVIdCp2YfTS0YNQBfsuyNpX/i6lN3cl7f3r8qz827TwZ/2ny/rSfzHNzTtKx7z9I2Ldp1ynzLD9PcupPH3126A5+ZIn8vJ/uSDVP92H8tj9NvfivI2czDTnx1T/MS5pz7MLfjeue+5t5Mf+M/IwywL44cMk8K9fsPPiNrCv/Y5pn+TDq1dg4PuyUj/3piQOf/JiRZJ6eazK/P3hw94fm6f6c/7526vj+dd9//aV5Vq65+vWXsu5//e2keZYP4/2x5w79qbdl8F1X+A36U3iN/pwkhpe+aM5JgryR0aVaUapVoECTtwlDbRobGytvDZxQO2d57E+Xrdpyd8XKrmxhYeHPvjDaw27r8Om/du01ICgoSK1b4/7aH312yLyY32bxuxvkab+5YqN5ljnbky8+M2iEdrdOvcZVqt1vXsyH8dv+9J0PtqoRohcz+y3zkubUbfCIYcVSZcqZF/PPJKf+JE9Y/ocyzzLn0OnfBg2dsPPgN+ruoOjxsu7uw38xL+nDqJdl4/iwU371p0f2flSoUMjqd2ebZ+Wc//n7qe5d2la5527zLH/O364ekvEzftTz5lnm/POXo5PHvXD1Ztk6aexgWfenS3vNS/ow3h977tCf2s6Ta63IAuwawvvoT+ErnrwwchEq5F3+VqsMSMAO8movbwf6AwuoTW9LXvrThO0nwgtH1KnbKP7T5K17zw8aOlE+Ob/w8iTzkuZ07vF0cHDw+Jj525K/Wr72i8pVaxSPKrnn6BXzkv6Z2+pPmzRvd3fFytrdF4dPHjB4lHkxH8Zv+9PRr7wmv2f5dcm40rJ280Hzkoac+PqfEUWKPVingX7FURNjzUv6Z26rPx0+bqYsLP8rqbtzl8Q/3vnJOzs42r6o12fj+LBTvvSnh/esKVWyuPx6b7c//c/fUwcO6C4rBnZ/Omf6SFn423NfqLsfr57/VO+O1344bF7Sh/H+2HMn//tT2d0xH1kJyGfRDP84siwlJYUhCjP/GaLySZIhCjMfDtH/+O7Kd2vX/GX1+4GUr1cs3//mG5KEua+tnT1r2dSpr02c8OrY3P/Xk8XMW3NmZFTI2DAOF8Az5tpUXuWoTe9AXvrT3k+/GBQUtGN/hjalTr3GUSVKmZc0ZNfhb4ODg3v2HaRNWbrqM/kQPmnGG+aF/TO31Z8+0qytvj/1w/htf/p4lz7Fo0qap+eazbvT5Q/0yvTF5lkFIrfVn748doa+P/XPqBdq4/iwUx7703/+cnTGlGGhoYVKREXebn96eM+aBvVqyatc8ciigd2fxr46Qt+f+me8P/bcyf/+1PK7mUC03xznyBCFOwxR+DlfDdFrJ0+au7PATg7VqkwxL+/YyNgwDhcgR1evXjXXpgkJCZzu/47lpT9dn3h03ltr9VOatXo0NDQs9eK/Tnz9zyEjY/oPHpmc+u+D4MbFzH9m0IikY98fTP91yXsJm3ae1GbFf5osH8JHjJ9lfhSV41/9MXPee+0f7/5w0za9nhr8ydaj+rlvrdzUqdtTDRo3b/dYN1lMO/Oj3Hh64IhV6/es3pDUucfTDR9u0aVn/w837ldzZy1YIc/nyNlM/aZemb74pRFT1O2j56/FzH6rdYcuasV312zXFtP3pwnbj8ujbPwiTZu78+A3MmXFx7vk9gsvT7rr7kpFikbKlGWrtsiUCVMXjJwwW1t4z9Er0aOmNmnRvnHT1k8/P/yzL09rs2T7svrh038dPm6mLCAZ/cprKWd+1xbYvDt9wOBRatbAIeMS913QZt1W/LY/rVq9ZpPm7czT07L/fPIr/eizw9qUN97dIFPU3/f1t9ZkdV4bkswrukvch9u69hrQqEmrjl37yt9XP0v+xH0HRDd6pKWMcPlb6Ee1/DVnzl++Pfliv+delnXbPtr19Tc/VLPk0eX5GM51u3TVZzJRO9Ta3dDV96fyF5dV5H8Z/XYGDZ0wfW6c3JBHV2cq6Pbkc+pfIGTYyPLyf5laModhvGnXKVly1+FvYxetkgXk+cv/Edp5ACT7TvwgY7XNo11lVs++g+R/Jf1zuK2oV2zj+LBTHvvTOdNHBgcHDxn05NaEZYb+9IP3Yke/PODEgU+0KZ998pZMObL3I3W3UKGQalUq7t66onXLRp70p7Lk8/27tWnZ+Ok+nTave1M/6/SRjS+/1E+283iH5vKU/nb1kDZrybyJ8qyuXNg1cugzsm6Pru3WfbBAzTq4+0N5PmePbdZvavumOJn4y+V96m7ip28/0/eJls0b9uzWflXcLO2kpfr+9D9+PS6rbFm/VL+dyeNeWPn2TLkhj970kXqy8KBneyxbNEWmyO9Klv/jpyNqyf/8PfXdN6d169ymVYtGzz3T9cvEFdpGLqRukSV/zEhas3KuLCDPf8zwZ7XzAEh+/+7g3JmjundpK7NeHNj7wK54/XO4rXh/7LljV3/64fod8iJONu86YZ7owCxe+n7Wjp2PPvkbMESJOQxRvw2voiq+HaKqP/1xy+bfDuwnGV/sME90YGQ80J/Cc1evXk1ISNCfPYPaNL/kpT81ZMXHu8LCCz/e+Ul1N3bh+6rT+fPuolVyV38aUC1Hzma2e6xbSEihhO0nzHPTsi8E1Lhpa1m90SMtez/94j1V7g0ODl6y/FM1t++AoTKr2r0PdO/z/EP1H5bbTZq3O3r+mtqy3H2kWdtChUIfbtqm7aNdwwtHyO11nx9Ju/kM5y/9SHug5NQfQ0PDevQdKLcPpf9Wp15j9aDdej9bqXJ1ua1Vq/r+1Hws6idbj8qUCVMXyO3OPZ8pWaqMPG6bm82a/vynn+5ILVm6rPze5Ll17Nq3eFRJuf3Wyk1q7qDo8cUio2T58hXu6dCpZ437a8tm23fsoa1btFjxivdUk9+J/J4ji0fJ3S1f3smfMtEv+9PDp/8aHBIif98xk+a27tClZdtOE6ct1PruzbvTC0cUkb+7uiqUulv7oYYyWtKy60UZJHEfbpO/poyHnn0HfbwlxfwQWl54eZIaRb36vVC/UTO5/eyLY9Ss1974QMZMqTLlOvd4ulW7J2Sg3l2xslZVy1/ngdr1o0qUqlmr7mOde5cue5fr5pHU25MvynPoOyBa/0AP1mkgA1jdzmHo6vtTy2NRZWDIiErLPsWBbCFr3Rbt1dkz9Oc/zXkYL1u1Re42bdlBRo78htX/ZeXKV1S/5EOnf5MhJw/UpWf/Pv2HyNMOCgp6/a01+qfhedRLt3F82CmP/eneHatOH9mobrhu7U+/Svu8SEThWjWr/39/PaHdbdyw9v+fmaYWWL70VTXLk/506sSXZPuytSGDnmzRrIHcnjB6oJr10ap5oaGF7ipXakC/Ll06tS5UKES2dvnsDjX3kcYPNahXq3SpqHoP1ezb6/Hyd5WWdVWPeeXCLhl7L7/UT/9AjRrUrlG9sro9fMjT6kEHP9ezycN15XaHtk3+8/fUG7f2p5bHokYVL9a9S1u5MX/2WNmCLPBou6byU9y49fyn//j5iDxDuSu/hIEDut9b7R65/eqkaLWRHZvj5O5j7ZsVjyzarXObtq2yxn+lCuWu/5b1e/vnL0cfqn2fPNBzz3Qd9uJT8rRl7H0S/2c7fLvx/thzx67+NF/ewknARN5xs0aFjz75GzBEiTkMUeLn8e0QVf3pz9u3yQ1CVGQ80J8iV9SmXpAvuw2vTF9cp24j+fTbovXjB07+ok1/vPOTMvHt1Z9v3Xu+SNHIWnXqq25Iy5cpV1q1e6JkqTIREUUNR/zpM3by6y7dd7EPn/5r1eo1y1e4J/Xiv97I7i4793xGO3BPfZc5etS0tJv9aUhIIe24udUbkmRKr6cGp2Uf1le0WHFVQqlMmfWm6+ZBi88Myvpe6rQ5y9Qs2VTLtp1kyoqPdqbdTn+aZvr+vr4/feDBeuGFI7TDab84cEmWjCwepU5eqYoweYaqIjzx9T+btuwQFBSkLsvep/+QsLBw7TBD9aAvDp+sPZDn8c/+VB2VLD9vpcrVW7TpqK5UVvuhhofSf1MLqL/XoKETZVzJb1L+mjLS1KxmrR6VFUNDwx5u2qZ+w6byi5JhMGfxavOjSNZuPigLy3BV3aukS8/+suXPk87tPvwX+QPVuL/23uNX1SwZAMEhIY2atFJ3VTupnVl134kfSpYuW7lqDXW3SfN2JUqW1ja7aedJWXj4uJlp2UfLutwPXc/7U21F7fv7+v4052Gs+tMKlapqF5tSp1J97Y0P5Pa8t9bK7Xc+2Kpmya+97F0V6jdqpj2H24p6DTeODzvlsT/VYu5PJXFLpsrEKeNf/M/fU+vXfaB4ZNFvzmw3r5trf3oseZ2Mvad6d9S61+ee6SpbvnR620+X9kZEhD9U+76/XjmgZskzCQkJbtOysbqr2sl5s8eou79/d7Bc2VL33VtF3e3QtkmZ0iW0zZ4/kXWOlDnTR97IPlo2a0Q93UU75lR9DX9mzLAbt9Ofaitq39/X96djhj8rt5cvfVXNuv7bic4dW8kU+Slu3OxPq1WpqF1sSp1K9aNV8+T2+g8Xyu2dW95Ts/7x85GKFcq1aNZAew63Fe+PPXfoT4k34ttP/gYMUWIOQ5T4eXw7RK/RnxJT6E+Rg8zMTENtOmbMmPj4+PT0dOOiyLN82W2IHjW1R9+B1e59ICy88LAx07Xpyak/litfsUKlqvUbNi1SNPLzPWcNK366I7VrrwHtH+8eGhp2X806nyedM288LbtkvLtiFf2UVev3zF0Sf+Tc32TdkJBCe45+p8068dU/ZGHVV6r+1PAF8KgSpbQpvZ4aLA+978QP6m7dBo+o5iv14r9KlCx9/wMP6VeUpydbkyeclk/9acL247LY088P1z/K9LlZtcLM+cvTbhZh+i+hT5rxhjal33Mvy+2psUtVu5qW3XDpN+V5/LM/XbL80/IV7okeNU3+HGnZf1nVBj498N9HMasDQh/v0kem6w+NbNqyQ437a2t16sYv0kqWLhteOGLX4W/ND9R/cFZxoz9/qNyWP8SuQ5dfmb5YZi2KW69fvnOPrGP31PLy15TNag2ppH3HHvKU1O3X3vhAPzYGDZ0YHBysThmc89DNl/4012Gs+lPV56okbD+hTVn4zidyW/7Xlv+R1dw7HmBpAdefStQBof2e7Chz3R0amWt/OnZEVsmoP3+o3F759swfLu5ZunCyzNr08RL98gP6ddGWf6TxQxER4VpDKundvYM8JXX7o1XzZMmtCcvU3SnjX5Sx991Xu+V2r25Zi/36bbK24n9fO1W1cgX1VPOlP/3fP9LLlC5Rt05N/YqXTm+Tuc/373bjZn+q+lyVM0c3aVM2fpz1QvfC870yvz+o5v7j5z/PCXAH8f7Yc4f+lHgjvv3kb8AQJeYwRImfx7dD9Br9KTGF/hRmmZmZSUlJsbGx6qNONLWpV+TjbsPxr/549Ile8qF36arPtInL134RFBTkunlEm7us3XwwNDSsTt1G5lmS8MIRbXVHiepTtXrNCpWqGia2e6ybPGLKmd9Vf6q+j6/l7oqVtYMH1RGOMbPfSrvZK6kzk36ZcsWlO/mAlsioEnXqNU7Lp/5UnaNz9sKV+ofYsO2YTBwUPT7tZhGmjjZVmTl/uUxRZ1bdvDs9qkQpuRsRUbRVuyemzHpTX8bdVvyzPzXn2IW/lypTrnTZu7Qpe45eKVm6rOvmMcU5ZOKri1zZdbN5VvPWj0UWjzJPlzz5TNZ3q7cnX9RPVJtSZ7OVv2alytX1c7v2GiBz1W0ZgbJldVKL1Iv/Kl/hnqYtO6hZOQ/dfOlPcx3Gqj+NXbRKmyUbcekO336wTtbXyYNDQuo1bDJszPRNu04ZNuV51Ku6cXzYye7+9JfL+8qVzfof8KWBvc1rqeTan3Z8tEWJqEjzdMnQF/rKxq9c2KWf+NaCrBNN7NgcdyO7P7232j36uc/3zxo/6vb1307Ilp/q3VFu/+8f6ZUrlX+sfTM164H7q1WrUlG/oqRnt/ay7n/8ejxf+tOfv9nnyj4vquFRSpYoLk/7xs3+dM3Kudos2Yjr5jGw8uQbNcg6XUlISHCzJvVmTxt+IXWLYVOex/tjzx36U+KN+PaTvwFDlJjDECV+Ht8O0Wv0p8QU+lNoqE19K393GzZ+kSafePv0H6JNif80OTgkxOXBldBV96pdV0fLia/+IdMf79LHvIqkQqWqNe6vbZioDg88fPqvqj/t/fSL+rlZ/ekjLbW71e59QH0lecjImODgYHXxnO3JF2XFfs+9bNhyufIVaz/UMC23/nTd50dcHvSnsxeulMUWvvOJ/iFUjfvcS2PTbv0itsqf/Wn2l6/TstrD70a/8po8/0KFQmV64YgiC99ep9+ahyko/amkUZOsrwDLqFB3k1N/kj+KS3daWHdZ8fEul+7Un/o0aNxc38nq0+3J52Qt7QhlFXWM8NL3s34o/dkYVPT9aVp2AxteOOJg+q/L12bVQ9rVpXIeurn2p/pTT7jrT3Mdxqo/1Z5S2r/706nq7tHz12YtWNG6Q5ciRbOuQR8UFKRG5h1Evbwbx4ed7O5P/3b1UKUK5WRW7+4dzGup5NqftmzesPxdpc3TJYOe7SEb//27Pw/AVFn5dtYJFrZtfOdGdn9a876q+rn6/vRGdgMbERH+x09HkrZnnetZu7pUtSoVH6p9n37FGzePbP3Xr8dy7U+LRxbNtT+9ciHrf7eRQ58xPIr8xho3rH3jZn+qPaUbN/vTGVOy+tMb2dee+uC92G6d20QWK6LG3sQxgwxb8zDeH3vu0J8Sb8S3n/wNGKLEHIYo8fP4doheoz8lptCf4vr164baVMTFxaWkpMgs49KwTV52G6JHTVXHqWlRfU2Xnv3V3UOnf7unyr13V6zStGWH8MIR2hXq124+2L3P8wnbj+vXVUWV5RXkS5Qs3fDhFvopsu60Ocv2HP2uXsMmxSKj1Pe7tdRv1KxI0ci0m9/fz7k/HTNprjqjaKXK1Zu3fkxNTDnze3BISOsOXfQrHj1/LTg4uHX7zmm3dqZL3ktw3foV7/fW7nB50J/GxSdmdRMx8/WP8u6a7TJxXPbEXPvTw6f/qm4cTP/1tTc+KFqseNm7Kui35mH8sz9dsOzjpweO0C4YpVK9Rq2oEqW0u4936VOoUKgaPDPnvacmJh37/oWXJxmq5PlLP3LdvLKTIR069ZSN6E/Oe+DkLxNfXZSw/cTgYa9ktTzZFxzT8uLwrC9WqwtS5dqfymiXu3MWr5YxH1k8Svtxch66+s5Unozr1vM8HP/qDxmKufanuQ7jXPtTbYDJI8qoVlcwu7NrlKkXeeP4sJPd/Wm/JzuGhhZSLeequFnmFW940J8+2eNR2Yi6cJPK339MeWvBpDNHN8VMyDr2Oe3QBv3y014ZIhNTD66/4UF/eix5ndxd+/7rg5/rWSIqUl2aSdKsSb2o4sX+9490/botmjWILFbkxq2dqTwZuT1qWH9tsf/620kZP7n2p//x6/GQkOBundvoH0J+TFm36xNZE3PtT//16zHtEfdsW/lQ7ftk7sVTW/Ub9DDeH3vu0J8Sb8S3n/wNGKLEHIYo8fP4doheoz8lptCfOtb169dTUlLi4uLU5xmF2tSH8rLb0LJtp7CwcHXApsq4mPku3cVqevYdFBQU9N7aHV8cuFS0WPEHHqynKqqPPsv6fN53wFBtxQMnfyl7V4Uy5e421Ekqj3XuHRoatuvQZW1Kr6cGh4QU2nfihyEjY2RT85d+pM3atDPr4337x7unedaf7j78F9mU6rzmvbVWm97w4Rby06lTVarIz+W6eSCtvj9VxxWOnfK6tuSgoRNcuv60acsOd91dSZurNW4H03+NiCha4/7aJ77+pza3Y9esL+2qcjnn/rRRk1aVKlfXr6t6au2u5/HP/nTE+FmuW89q+uHG/TKitIJ+zuLVsoCMgRNf/aNO3UZFikaqE57KMFO/WO3STJIWrR+XgWF5jl11HaoFyz7Wpqjzli55L0H9cfsOiNZmycZlCJUsVUYdA5trfyqRZ9Lm0a6RxaP0h2bnPHT1/alqS1u1e0Jb8u3Vn7uyLyym7o6cMFvuaid71Q+bnIdxzv2p/Fd+29uTv9bmqgdas+mANsXzqFd74/iwk6396dr3sy5qN33y0P++durhRnUiixW5s+tHqetQJaxdrE1R5y3dsn6pOmj05Zf6abP+8/dU2VrZMiXlQW940J9K6jxYo3uXtiWiIoe9+JQ2UZ62LLZhzSJtyvkTn8kY69Ut60BafX+q2tIunVprS37x2bsyV+tP584cJXe1n11//ahWLRqFh4epM66qLF/6qsxdunDyjdz6U/mvjL2/nN+pzVUPdHTfx9oUz+P9secO/SnxRnz7yd+AIUrMYYgSP49vh+g1+lNiCv2p01Cb+q287DbEf5pcqFDoAw/We2/tjo1fpI2aGBsWFn7/Aw+pyxmpy4s/+cxLauFpr70tdwcOGafutu7QRT6Wjxg/a9POk7L6Q/Ufdt3a4+izYdsx2XLV6jXfXLExYfuJkRNmy7pPPTssLftIwzLl7i5SNHLKrDcTth+fv/SjuytWDgsvrPpHT/pTSev2nWWxyKgS2oWYJCvX7Q4OCalc9b7F726QJzBh6oLwwhFy99DprEvo6PtTeZTiUSVLlCw9Z/HqDxL2vTh8sjojp9afPt75SdmU/LDquk/6xk1VhC3adJSHW7v5YN8B0XK3U7en1Nyc+9PJM5fI7Se691uz6YD8/sfFzJdH6db7WW1hz+Of/emeo1fkjxtVolTswvc3705/7Y0PSpe9S+6qs5HKf4tFRt1Xs466dtOnO1JDQ8PqNWyiak11SOajT/T6eEuK/GK79OzvunlWBHPUgdKyNRmlsp25S+JLlipTq059Vfc3bdkhKCjo2RdGf/TZYRmr6gQCsqRa15P+VP27gpBnok3MeegavrOvyl8ZLfI/nTx0qTLlIov/+/ynMbOzLqfevc/zqgLWD5uch3HO/en6xKPyP/iDdRrExSfK73/h2+vkcStXraFvpT2Petk3jg872defXrmwK6p4sYdq36+u3XT22OawsNBmTeqpWlOfXPvTf/5ytEb1yrK1Fcumy3Y+Xj2/bJmSDes/qI5Ifax9Mxl740c9f+LAJ3u2rWzTsnHW//7Lpqt1PelPF8/N+rcccSx5nTbxt7/sr3B32chiReKWTD19ZOOGNYvkSRYuHCa3b5i+s9/psRYy9l6bMTIlaa089F3lSpWIitT603ffnCYLD36up6qA9f1p8herQ0KC769RZfO6N9OPfLpk3sSIiHC5Kz/yjdz601MpCaGhhRo1qL3r8+VfpX3+6UdvyOPed2+V//rbSW15z+P9secO/SnxRnz7yd+AIUrMYYgSP49vh+g1+lNiCv2pc5hr09jY2KSkpMzMTOOi8IU87jYsiltf9q4K6vO5fMbu0Kln0rHv07Kvv1SyVJm7K1bWX7C7SfN2soy69tH+tJ+yWsXgYLWubMRdeaqy4qOdVardrxYuVCi0/+CR2hXPP99zVlVaSq069bXj4zzsT+WncN16PKzKslVbKletoTYbHBLS7rFuWpVpOOep3ChTtrxaUlZRl6XS+tP31u6ILB7lunkWV0PjNnbK65FRJdS6EUWKPffSWO2L5Dn3p5KXRkyRVdS68mvp2muA9oXr24p/9qdp2a1ow4dbqB9Q1G3wiEyR6Se+/qf8EUNCCum/Wa8602FjpqdlX6xp5ITZ6qydQm7IdMujm1W2J1+U8ak9UKt2T2gHVh86/VuPvgPVGWZFufIVZy1Yoa3oSX+65+gVWf3e+x7UT0zLcega+tMtX555oHZ9tZiMJXkC9Rs21frTXYe/Vf93qLO4GoZNDsM45/40LfsUCuUr3KPWFfK3kOesLXxbUa//xvFhJ5v60//5+6nWLRsVKhSi/2a9+hr77GnDDevm2p/eyG5jO7Rtov2Su3RqffXrL9Wsf/5y9IXne4WGFlKzKlUo98F7sdqKnvSnv1zeJ6vXrnWvfqIkIz1RtbFKw/oPaod2GvrTi6e2NqhXSy1WIipSnkDzpvW1/vTHjCR5DjJLncVV35/eyC5J77u3ilo3JCS4Z7f2+lku9/2pJGHt4sqV/nxRFa1aNJLnrC18W/H+2HOH/pR4I7795G/AECXmMESJn8e3Q/Qa/Skxhf404KWnp8fHx48ZM0Z9bommNvVXed9tOPH1Pzd+kbZ280HDNXY8yd7jV9dsOrBp58kcii19tu49n7D9eMqZ382zvky58vGWFP3JBPIr8qCfbD2anPqjeZY+J776x6c7UjfvTrf8WY5d+Puuw99qna8hx7/6Q9aVWP5oOefIub9t+fLM+sSjB9N/Nc/1MH7bn6okp/4kfwL9CRw8jPxRPt9zNmH7Cf2RxTlERpG7v7X8emWWbE1/woR8iedDN3HfhQ3bjhlOCKsio2734b+oo0ot4+EwNkd+3h37M9Z9fkSep3mu51FvBMbxYaf86k+9k5+/2XfycELm97dcLUrlj5+OyKyM9MT/+bvx+NY8Rh409eB6ra7NIZfP7kg/8ql2BlV9/veP9J8u7VVHlVrmmzPb3f1oOUd+3u++2p12aIM8T/Ncz+P9secO/SnxRnz7yd+AIUrMYYgSP49vh+g1+lNiCv1poKI2LXDYbSBpft+fkgCIekcwjg87Faz+lNgX7489d+hPiTfi20/+BgxRYg5DlPh5fDtEr9GfElMCqT9V15FPdLzY2Njhw4erTylizJgx8+bN27Bhg3G5gJaSkmIcH36P3QaSRn9K7I96azCODzupsUd/Srw/9tyhPyXeiG8/+RswRIk5DFHi5/HtEL1Gf0pMCaT+NCUlRe2aA9H+8QnttrDbQNLoT4n98f4rJP0pUfH+2HOH/pR4I7795G/AECXmMESJn8e3Q/Qa/SkxJZD6U/UJbeacBfI/mpOzeOn7b8bFm6c7J+MnTvGTT2i3hd0GkkZ/SuyP9zss+lOi4v2x5w79KfFGVvj0k78BQ5SYwxAlfh7fDtFr9KfElMDrT2kHyMw5C/zkE9ptYbeBpNGfEvvj/Q6L/pSoeH/suUN/SrwR337yN2CIEnMYosTP49sheo3+lJhCf0oCL/SnpOCG/pTYHe93WPSnRMX7Y88d+lPijfj2k78BQ5SYwxAlfh7fDtFr9KfEFPpTEnihPyUFN/SnxO54v8OiPyUq3h977tCfEm/Et5/8DRiixByGKPHz+HaIXqM/JabQn5LAC/0pKbihPyV2x/sdFv0pUfH+2HOH/pR4I7795G/AECXmMESJn8e3Q/Qa/Skxhf6UBF7oT0nBDf0psTve77DoT4mK98eeO/SnxBvx7Sd/A4YoMYchSvw8vh2i1+hPiSn0pyTwQn9KCm7oT4nd8X6HRX9KVLw/9tyhPyXeiG8/+RswRIk5DFHi5/HtEL1Gf0pMoT8lgRf6U1JwQ39K7I73Oyz6U6Li/bHnDv0p8UZ8+8nfgCFKzGGIEj+Pb4foNfpTYgr9KQm80J+Sghv6U2J3vN9h0Z8SFe+PPXfoT4k34ttP/gYMUWIOQ5T4eXw7RK/RnxJT6E9J4IX+lBTc0J8Su+P9Dov+lKh4f+y5Q39KvBHffvI3YIgScxiixM/j2yF6jf6UmEJ/SgIv9Kek4Ib+lNgd73dY9KdExftjzx36U+KN+PaTvwFDlJjDECV+Ht8O0Wv0p8SU/8feeYBVca3rn46ggJVE7L13Y4tdY4y9xK5Ro2LHXrBL7GJs2XbFrthFjQUU2xYFIsZEIycnjTQTCScnud77P/denvv/2N9hZTkDm02ZPbu8v+d9eGZWmZk9+9sza72sWQP/FHI8wT+F7FfwTyGtZX0PC/4pxLJ+7GUH/FPIGtK3568AIQqphRCFbFz6hmga/FNIJfinkOMJ/ilkv4J/Cmkt63tY8E8hlvVjLzvgn0LWkL49fwUIUUgthChk49I3RNPgn0IqwT+FHE/wTyH7FfxTSGtZ38OCfwqxrB972QH/FLKG9O35K0CIQmohRCEbl74hmgb/FFIJ/inkeIJ/Ctmv4J9CWsv6Hhb8U4hl/djLDvinkDWkb89fAUIUUgshCtm49A3RNPinkErwTyHHE/xTyH4F/xTSWtb3sOCfQizrx152wD+FrCF9e/4KEKKQWghRyMalb4imwT+FVIJ/Cjme4J9C9iv4p5DWsr6HBf8UYlk/9rID/ilkDenb81eAEIXUQohCNi59QzQN/imkEvxTyPEE/xSyX8E/hbSW9T0s+KcQy/qxlx3wTyFrSN+evwKEKKQWQhSycekbomnwTyGV4J9Cjif4p5D9Cv4ppLWs72HBP4VY1o+97IB/CllD+vb8FSBEIbUQopCNS98QTYN/CqkE/xRyPME/hexX8E8hrWV9Dwv+KcSyfuxlB/xTyBrSt+evACEKqYUQhWxc+oZoGvxTSCX4p5DjCf4pZL+CfwppLet7WPBPIZb1Yy874J9C1pC+PX8FCFFILYQoZOPSN0TT4J9CKsE/hRxP8E8h+xX8U0hrWd/Dgn8Ksawfe9lh0/7p5j1nxocsUqdbovzU1Vcbd54cPHLyjfjv1VlZKv7pP42fvuDlLXvOUt2rd79WF9NX+vb8FeQ/ROmEL1m1bejoKaPGz9515Kq6gBnlp66NCCGqNfkPUQqtpWt2KHQ76bm6pFr5qWsjQogWOGkF5J8+uXJlxayZ4wYPWjR5UsLZs+oCZpSfug6jlbNmzp8wnpdPbN06fuiQL65dUxezjuCfQo4n+KeQ/Qr+KaS1rO9hwT+FWNaPveywXf/01JWHAUWLV69ZT52Vo/JTV3eNCp7p4uJyIfapOkutyIvx9DEPnbnDq8FTF1BdSlSX1Ff69vwV5DNEo248eT2onJu7O515WqAT3q3PkIRnf6pLqpWfurYjhKjW5DNESfUavuGi4lz0Y3VJtfJT10aEEC1w0grCPz2ycWNhHx9So9q1i/r7e3p4hIfOVxfLUvmp60hqUrduudKleXne+GCKVeOJE+pi1hH8U8jxBP8Usl/BP4W0lvU9LPinEMv6sZcdNuqf7j56rUSp16hjkAcPND91bUG56vlPn7eKCoue//EL98PW77mZ+KO6pL7St+evIJ8hWr9RM+9CPvtP3qTlhGd/jho/i76CxSsN6pJq5aeu7QghqjX5DNH4L/7w8Sncsk3niBOxsu599pu6cAHWtR0hRAuctHz7p19ev+5XuHCNypWfRUfT6vd37rR54w13NzdLRpLmp66DSfZP7xw/vuODsG9v3VIXs47gn0KOJ/inkP0K/imktazvYcE/hVjWj73ssDn/9E7SLwOGBbu5u5erUCW3Y0jzXDf+iz+yGwNI6fef/EOdLivHAtkp/uk/adeKxPz0/LNTjkdIR5LdGaAjpFx1eq6kb89fQX5ClL4XV1fXgcMniBTjpy/c3NzaduquLlyAdc2EKCnu8zR1oqwcAyA7IUR1IT8hSjp9NYnOecicFeqsHJXnughRxw7RtHz7p4bly+g8H1i/XqScMhgoZcWsmerCBVg3NSGBJKe8eHVVoZ/j4tSJedOv8fHqRKHfEhN/efBAnS6UZa7sn2Yn2rKZXdPHpwLq9DwI/inkeIJ/Ctmv4J9CWsv6Hhb8U4hl/djLDpvzT4+ej/Pw8Bz83qTbSc/Llq8se6DUNX27x4AaterLHd0Bw8ZTyt7j183XzU7h247XbdCUankX8mnYpKU8H2XEidjmb3b08vKmTlrpMhUmTl/y4OnvIpc2Hrp8E4myqIBfQFHa773HqZS1adcpyl330VF5Rxt3nqTErXvP8WrY+j206urq6ubmVrNOw7VbDouScs+fPhcV+3B7pLypWnUbDh8zjRbob8nA0lS4fMVqb7btQil0PFT+XMxnXPLeZ7+NmxL6WumyVMbLuxCVOXLunthO7wEjSfSRa9dtRIdBn7Rd5x4XY78QBdYbjtGxUTodZ6UqNWcvXCcfRq6kb89fQT5DlKIrNuEHsXrN+A2d3i7d+idaEKJm6mYnMyF6+faX3fsM9fMPoI34BxTrM2BUdNw3Ihchmis5Uoiu2nSAzqdh/wV1Vo4haqZudkKIOkOIpuXbPyV9ce2a7E7uXZ3hXIcvCKXlqF276lavPrxPb5F7NzKyfo0aQ3r2YLPPTF21KL1hrVpJFy683aaNJ4Wmh0f39u2/io29fuhQk7p16Zsq6u8/YdhQ2WTct2Z18wYNvL28aLOFfXzeads2/swZzlo+fRodm7yvj5YtpZQ1c+dkuWvKooOnrdGOaFO9O3d+/PHHcpmr+/e3b96c91U+KGjBpInykdDHnBs8rsxrGY/RFPLy6tyq1c2jR0Wu7J/yvj45f14sf3rpUrd27WjLFG+Nate+sDvD3BQ6sXUrJXq4u/t4ew/q3i1y6xaqcu3AAblMrgT/FHI8wT+F7FfwTyGtZX0PC/4pxLJ+7GWHzfmnsQk/XIx9xstqD5Q6rtQVqlq9Do9mClu3mzoY1Ce3pK5a85Z+6GJ6zJ/6tPOXbSxXoQr1cvnZakNElLu7R/GSgZNmLF24Ymvr9l2pJHWMxfAiWqVdFPELGDZ66ozQNXUbNKWUUeNnU9atT34u5OPbsk1neV9tO3bz8Sl8J+kXWqbeOBWmDvycxeGzFqylj0OrtBEuKff8t+w5S8tL1+yQN+Xm7t7x7d6JJtvizXZvU4GBwyfQ8Se+OnPfg6e/N2vZnlY7dum1aOVH40MW+QcUo/7/3uMxvJ0mzVqXei2Ijqpb78EzQ9d06dafCtdv1Ixzdx6+QmeAtrB4pWHp6u1vtGhHuTMzDzK30rfnryCfISrr41t/oy+avhE6XZxiPkTN11XLTIhSdfr6aF9DRk2mL4hHXgeVrXj9QQrXRYjmSo4UojwvRMicFTXrNPQt7EdhQKddjAA1H6Lm66qFEHWSEE0rCP9UVsyhQ+VKly5VvPjfb9zglF6dOtH52b1qJS3/FBdXq0qVwj4+wsQ0X1eheeOD3dzcqMxbb775wcwZPTp0oC23eeONAD+/Yb17rZg1s1n9+pSydt5cLh++ICOcWjVu/OHCBR8tW0plqHrtqlXZuk25c6dCUJBvoULsVNIh0TLl/pTVSFWek7Rk8eJdWrfet2b1osmTfLy9y7z22texsVzg9LZtHu7udPALJ0/auGghFaPy3dq14339Gh/ftlkzSunRseOmxYvmTxhf1N+/kJfX5X37uHp285/ycsWyZVs3bbpsWsik4cOoFp1Asd8jGze6u7lVLl9+1exZdAYqlilDZ4OqnNm+Xf0pLBT8U8jxBP8Usl/BP4W0lvU9LPinEMv6sZcdNuefysrSA506O4xa/NR5jrrxpHAR/yrVaov3JudYVxb1z6nTS91vHu5EunQzmXr+7Tv3iP/iD+rk+/gWof6/KN/r3fdov2s2H+JVWvbw8Dxz7RGv0kaKlwwMfL0Mr1Jfmvrn14z/HmkVc/87Ktyz33BaPhfzGWXVrd9EWBJ3H/1KnX9PT6/Lt79MzE3PP1H15Knc86datDx45GRR8ey1T+kD0hnjVer5Z2x89XZRoF3nHpTCA6/6DnqfltmqSDQ9flujVv3eA0aKwrmSvj1/BQUSohR1pV4LcjWxZNU2OSvHEDVTV5aZEKXlHn2H0V42bDsuyi9ft4tSBgwL5lWEaK7kSCHask1nOjPFipccOHzC0NFTKlWpSastWncSj5CbCdEc68pCiDpPiKYVnH8atWtX8aJF6cwE+PnJIyu/io0tHRhIWV9evz520EAqsC1suYV1FWIz8d2uXXn1t8TESuUy3tf3wcwZnPL9nTuFvLzeaduWV8u+/nrFsmWf378vtjCoezcqL9zby/v2ubu5tWrcmMo0qFnTt1Ch+6dOKXbK4l13btVKPCN/MDycUmaOeT/NNJlAhaCgwr6+n12+LKoM692LCkSsXZNmGtlKy+OHDhG5CWfPent51axcmVfN+6eDe3QXFVfOyvgVbF6yOM10BqhWyeLFv7l5k3PpbNOqC/zTTOAOQCz4p5D9Cv4ppLUyHCzrXiHhn0Is68dedtiffxr/xR/UZaUeLHVEvQv5nLiUoK6YXV1Z67YeoZ7DopUfyYkHT9++GPvs8Fmji2k4kpx16Wayi/SoNS03bNJSLtCyTWdPTy9e3nHwYyowa8FaXp27ZAOt8mOtM0LX0DLtXa7LQ8BCl29KLLief9uOGd2/mPvfyXXZBT515WGiqefv7u4hvxZmyqzllLsv8gYtj508n5apfHZnOFfSt+evoEBC9PqDFDpdk2YsrVi5Bn0jC8I2i6wcQ9RMXVlmQpR24ecfULV6HTkr4dmfFPbFSwbyKkI0V3KkEB00YmLbTt3Fib3/5B9tOryT8TVl2nxmQjTHurIQos4TomkF55/GHDq0YtbMaaNGBvj5FfX3l58fP7N9u6ura5O6dem8DenZI1d1ZbGZePKjj0TKO23bUsrTq1dFSsWyZRvVrs3L392+/eTKFZH1IiFh/NAhVD72yBGROHvsGErhY/to2VKRrhDv+uKrD86XDwqqVrEiLdAGKXfsoIFy7uOPM0K9b5cutNzVdJxfXr8uF2CD9cHp02k5+adnJTP06v79lLJ4ymRavnH4MC3PeH+0vFmuAv+UgTsAseCfQvYr+KeQ1spwsKx7hYR/CrGsH3vZYX/+KYk6576FM547m7f0Q3Wu+bpCIXNW0Bb2HItWZ63ZfCjLjdNO69RrzMtUoPM7feXcdp17UJ+clxOe/RlUtmKtug15tW79JnQ8/Oz/gGGmft3HiXJddmxHjJ2eWHA9/8pVaxUrXlKumJjpO2zenXH/o56/osCsBWvFOYm5/x0/EksEvl6m78DROw5+rNia5dK356+gAEM00TRorkq12p6eXpduJotES0I0u7pCZkI0Ou5bF1UEJmbaPXcf/ZqIEM2lHDhEE03vlKez1OGtniLFwhDNsq4QQtR5QjSt4PxTofgzZ3y8vatXqiQnsnFZrnTp7+/eVVcxX1eIncG7kZEipWfHjpQivzepaoUKDWvVkjcYOnHCu127Nqpd27dQIf7Wrh86JAr8Gh9P5SmxV6dO8r4U4l0rJhZo16yZp4cH7T1ibUbwrJ8/T1GriK8vm7k1KlcuUayYIveDmTOo1vEtm9Ny8k/jTp4UtW4dO0YpCyZNpOU9puliFbYvzyEL/5SBOwCx7No/DV24jI4fclpRAARr6Z/OnD1fvVPIqWSysKx6heTYC503c/2aZZAzy/qxlx126Z9GnIil3q+Lqu9tSV2hCdMWu2SOElJo5YcRLqpBVSQ//4B6Dd/gZRfVa3/knn9iRid8oYtpjNKZa49oYeL0JZzOT3Sei34s1z0WFeeSMfHfrERLev5ubpb0/MtXrFbqtSC5Imn2wnVUgF/AQj1/MRCMJff8E01Dz8K3He/ZfwS/O8Ulm3k8LZG+PX8FBRiirJkmP2XFhn0ixZIQza6ukJkQvXL3K8p6p9cgRXp707PDPBrOBSGaGzl2iN599CudnEZNW4kUy0NUXVcIIeo8IZqmgX+aljksVB77yfOBenl63jp2TF3efF0hNhN5wCaL/VO5jOyfrpg109XVtbCPT6vGjccOGmhYvix4yGCXV/3Tr2NjgwIDKbFimTLf37kjb0oW71ph/vJrrH5LTNy9aiXlblq8SFErwM+vab16tFClfPnSgYGK3FWzM2Yl5uG05v1T+SPL/umODzKm7KC/8mbZVIV/ysA/hVhsECjjw+YxGAzctwSAgkEZH/nGaDQqdwOcGGV8aAliD8go40MP7M8/vZP0Cz8B2n/IWGr6L1+3S10xu7qyFq80UPVVmw7IidTHnhG6hrq+Lpn9cKHYhB9cpEFYLjn1/C/GfkH9sfEhi8ZOnk99dTHAcOL0JS6ZfW+hdR8dpcTZi9Ynvtrz/2jfeVpe+MEWUfJG/PeUYknPv1nL9rRfxcybg0ZMpALHL9xPzKnn/+Dp71fufiWyjkXF8UCq89c/l6tYKH17/gryE6J7j19v3b7rtgMX5UT64uRQzC5ELakry0yI3n/yDy/vQsLNF6LvyC+gKC8jRHMlhwnRmPvf9Rkwir8pIfYfu3R/l1ezC1FL6spCiDpPiKbl2z/dtHhRu2bNxOuMWPxmp08vXeLV8NCMGQ+Chwwu6u9fo3Jl8YImS+rKUpuJZvzTT86fpzCj5W9v3RK5E4cNdXnVP+3bpQsVmzMu4yczrHcveVOyeNeK2VGrV6pUuXx5Wvh4717KnT56lJxLn4sS6ePQcttmzSjkfrx3Ty4wbvAgKnDn+PG0vPqn0QcP0vIs0xysQvMnjHeBf5oJ/FOIZaf+6cuXL5OdnqMmlKnOBwWDMj4KAuVunI+EhISFCxcqU52PlJQUZXBojPIInA/EHmP92MsS+/NPew8YSS3+9YZj9x6nVqxco3ARf+4kW1JXFtWiXgq/54R199GvJUu93rBxi3uf/RZQtDgt80OmLO6xL1yxlVddcur5J5r63jVq1a9QqXrzNzuKROqlU9120n65JCWevZZx0uSe//6TN2l52OipoiS/z0T0/PlJ0ogTsbwq9/zZFBCzB5JuJv7oH1As8PUy/Ays+Z5/wyYtqbBsHAwfM01sPLfSt+evID8heurKQzoJbTt1FynxX/xRt0FTiqWoG084JbsQtaSuLDMhSsut23elzvyRc/dELvv+XXsO5FUXhGhu5DAhSkHFl6/bSc9F4uCRk+nMrN50kFezC1FL6spCiDpPiKbl2z/ld9yvmDVTpDy5csW/SJEKQUG8mnD2rG+hQg1q1vw1Pp5fozRu8CAL6yqkNhPN+Kdntm+nrNljx4isn+PiKpcvT4lX9+/nFB6/GTxkMC0P6ZkxhvrA+vXy1oR411NHvidSeB7SKe+N4C0XCwh4rWTJH4xGUWDBpAxHfuOihbS8fHpGhKyUPum3t24V9fcPCgzkyQfy5p/ye6tKBwaKiQVos3QYLvBPM4F/CrHs1D8FRFhYWGhoqDIVgAKCbxOpqanKDAA0BrFnU9iZf7ph23EXaSTUvsgb1HunPip1+3Osq9a7Q8fR1nr2G773+HXq7rZs05m2xmOaQpdvoqw69Rpv3n2a+uoZo5/c3akbL94TknEYOfX8V2zY52Ji5YcRcjpVpMRuvQfvPnpt15Grnd/pS6t0MJwr9/zjn/6TOupe3oXmLA7feejylFnLi/gFkETPn42A1u27zl+2MfHVnv/tpOflKlTx8PCcOH3JoTN3P9xxolqNjBdfiMFi5nv+Yev30HLzNzvSfmmDi1cafAv70Rlg1yC30rfnryCfIdqz/wj+vigwth+8xO8r51kXE3MKUfN11TITokfO3fPy8qZvcNnanbRMX5B/QDH6jsRDzRmHgRC1WI4UonSqXUxP3FOM7T95c/B7k1xdXSl4+MyYD1HzddVCiDpJiKbl2z/9wWisWbmyj7f3ylkzbx87tn/dusrly1O0HNm4Mc00u2ij2rW9PD3FpKWdW7Wi2DtlMORYVy21mWjGP/1bTAxtOSgw8Oz27Z9fvnxux44WDRtSCpU/vW0bFfj00iX/IkUqli3Lpuc3N2++XqpUsYAA+W1Uil27u7nRwo3Dhw3Ll1HJksWLi1dCsRdMH/b4ls3XDx2aM24sFa5XvfrPpsG2KXfuVCpXztPDY8GkiVSdPmCdatWo/N7Vq7h63vxTEu2OdkR1F06etHjKZPo4PM2r/Mqp3Ar+KeR4gn9qp6SkpASbsJEBSsDxCA0NpQCLjIxUZgCgMYg9m8Ke/NNrxm+KFS9ZrESp6w9SROKw0VOpAzB55jLzdbPUg6e/jxo/q5CPr4uJkoGl5TFW1NctUeo1zvL09OreZ2hswg8i18WCnv+9x6l+/gEkWpDT4z5Po8P2LuTDG/cPKEadc2EByz1/0vaDl14PKscl6Xg+3B5Jn0v0/G/Ef0+rlEX9zDuPfpF7/qQrd/7etmM36mRy9XIVqqw3HBOHYb7nn2iampOOjevS9lu1fevjW3+Ty1sufXv+CvIZovRt9hs8xsPDk88Mfb8hc1awIZJjiJqpm6XMh+je49f522cav/Gm/JpvF4RobuRIIZpoerNTEb8APjN0+eo7cDSPps8xRM3UzVIIUScJ0bR8+6ekRxcvtm3WjM8GUbFs2WObN3EWPxcfOnGCKPzZ5cv+RYq8VrIkD5k0U1cttZloxj8lHVi/vmTx4rzlQl5eYwYN5BfWTx4xPDUhoVXjxvTdXdydYRSyjmzM+DdDx5Yt5RdSsXjXq2bPKhbw7x9Ri4YNE86elctsXrI4sEQJzvXy9Bzco7s8NcGTK1e6tm0rQq5SuXIHw8NFbp79U9LJjz5qWq8efcDiRYuOGzxo8ZSMoeUf790rCuRW8E+z1PnrnyvebmehqDFAFyvFtc7GFf/0n4fO3MnbZe3y7S+pLt1E1Fk6Cv6pnRIZGWmyT4MjIiKUeQDkG2HQY4wzsDKIPVvDpv1T64iaqscv3Kf2rnoQK6WcvppEufLTrAUl2m/kxQenrjyM+zxNnSuLmtRU7MSlhOxamdeM39z65Gd1OutG/PdHzt27EPvUjE+XnWiPUTeeUHXZbcmD9O35KyiQEKUTTu1+io37T/6hzjWv3NY1E6Ik6rfQFxQd9406K59CiOpFgYQofWvHouIOnblr5sxnp9zWRYiakWOEaFpB+Kesp1evRh88qLAULVR+6prXLw8e0GbvHD+umHs0txI+5s9xcbePHfv88mV1mTTTA/XxZ+g2cDwlm1dRfRUbe/Po0aQLF9QWbR5Eu1O/aIsPlQ5DXd5CwT9Vi64VpcuU79F3mDorR/FcIuIfP3ah2IQf6ZiHvx+izlKLrp9LV2/fuPMkr/I/uq7e/VpdUkfBP7VTeHwWERISoswDIN9ERERwgBHJycnKbAA0A7Fna8A/hawhfXv+ChCikFoIUcjGpW+IphWcf+rYUo8DtQX9Gh/v6eHRuVUrkfJTXFz1SpWKBQS8SEhQl7dQ8E8VunL3q5p1GlIA5ME/DV2+iQcdO7B/uvd4DBVeumYHr678MKJLt/43E39Ul9RR8E/tkaSkJOEvELSqLAFA/ggJCREBhjHOwJog9mwN+KeQNaRvz18BQhRSCyEK2bj0DdE0+KeWyTb9U9LQXj3pwLq1a7d6zuwlU6fUrV7d1dX1o2VL1SUtF/xToYRnf4at31OsRCk//4x5G3Lln16MfcYTOnNdB/ZP+RWCwj+1TcE/tUfk8VmEwWBQlgAgHygMeoxxBlYDsWeDwD+FrCF9e/4KEKKQWghRyMalb4imwT+1TFuWLmneoMGnly6ps/TV8/v3P5g5o2716n6FCxcvWrRds2Yntm5VF8uV4J8K8cjKJs3bnIv5TOGfUtbgkZPFS+dIJy4lUMoH4Xt5tXmrDr6F/WYvXDduSsa7xXL0Tw37L9D2aV9v9xgQvu24nBV58cGAYcFNmrVu2abzpBlLYxP+Gto5a8HapWt2XIx9NmjERKrbvnOPVRv3cxYfoTw5NWnznjOUKOZd2bTrVNeeAxs1bdXhrZ5LV28XE6Eo/NNpc1fOCF0jb2fxSsOo8bMSTeYpv+iv+ZsdacuUsnHnSVoQE2TFfZ4WunxT207dG7/xZvc+Q7cduCg2curKQyp55e5XYev3UAE6/iGjJl++/aUoQMdDn65L93epbpdu/Vds2JflZDKWCP6p3fHy5Ut5fBZDicpyAOQVg8GgCDCj0agsBIAGIPZsEPinkDWkb89fAUIUUgshCtm49A3RNPinkErwT4XOX/98065TvKzwT+89Tq1SrbaXd6Ez1x7xatXqdfz8Ay7GfsEFVm06wDYlv7zOvH/6/sS5VKZSlZp9B73fsEnLDO9yzDTOWrFhn4eHZ/GSgd16D27T4R13d4/SZcpH3XjCuXUbNK1Zp2FA0eI1atV/q1s/fj/q3CUbEk0DYN3c3AYMC5Z3VLtuo3IVqvDygGHjeae93n2vXsM3MjzQVh141mmFf1q9Zr1adRvK22nXuUcRvwBa2Lz7dKOmrahwzdoNKDHx1flP7yT9QkeY4UE3a92z3/Cy5SvT8tjJ83kjW/acpdUWrTsVLuLftlP3pi3a0mrg62XEu7bowOjz0mYHDp/QsHELyn136Dj5MCwX/FO7w2g0/mUtZAKLARQUL1++VIYXxjgDq4DYs03gn0LWkL49fwUIUUgthChk49I3RNPgn0IqwT/NUgr/lHQsKs7T06th4xYJz/5kL3Ld1iPqijn6pwdP33Z1de3Srb94+WT3PkOpyrmYz67e/dq7kE/V6nXEe+p2Hrrs5u7epHkbXmV3cursMF69Ef99sRKlylesyqvNW3UoWqyE2Oypy59Q4UkzltLyhm3HablbnyFizOnE6UsoJXjqwsTc+KeJquf3Zf90yKjJtLzwgy2cde9xauv2XSmFPkVipn8aVLaieNkUHRulrNiwj5aj476h5VHjZ8s79fIudCfpF5FiueCf2h3q8VlEWFiYshwAeSJLgz4YY5yB9iD2bBP4p5A1pG/PXwFCFFILIQrZuPQN0TT4p5BK8E+zlNo/JU2ftyrDhew92CX7oZE5+qdDR09RFKDlxSsNV+78fc7icMpabzgml+fdcfm6DZp6F/IRDimp49u93d09eHnFhn1UcuPOk7w6avxsNze3SzeTM4p16UXFouO+FRXjn/6zdJkKpcuUTywg/zTh2Z9Fi5WgunJFeSYE9k/Zz2VFXowXKdcfpLi6utap1/hc9GPONX76As/vOwmpqal/mQqvQlnK0gDknrCwMGVsmYiJiVEWBaBAQezZJvBPIWtI356/AoQopBZCFLJx6RuiafBPIZXgn2apLP3ThGd/8lPnVavXEU+dK5Sjf9qq7Vt+/v/2IhXqP2Qs1b0Y+0xOnL1oPSVu2XM20eSfli1fWc6lg6RcXqZDoi136dY/0XSorweVa9G6E2dVrFwjqGxFuSKpw1sZryMzfvqiQPzTa8aMAaQ9+49Q7MUvoCgddmKmfxq2fo/IorPkkjkGNjFzhgGCjnbIqMk7D1+hT6HYmoWCf2pfxMTE/GUqvIpet0vgSJgx6DHGGWgKYs9mgX8KWUP69vwVIEQhtRCikI1L3xBNg38KqQT/NEu5ZOWfktp0eIeySpepcOuTn9W5iRb4p42atipR6jV1Oqln/xFU90b893Li4pUGSty8O+ND1W3QtEKl6nKu7J8mmhxY70I+t5Oe7zj4MaWLt0sFla1YtXoduWJi5sjWu49+zdE/bdupe47+6cXYZ7QwaMRExV4CXy9Tp17jxEz/VBxS4l/+6QKRYoiIopNQMrB0ho3q4tK0RVs6PMUGLRH8U/siNTU1ORN2FsRqSkqKsjQAueTly5ciongwYExMjEhRlgag4EDs2SzwTyFrSN+evwKEKKQWQhSycekbomnwTyGV4J9mKZes/NPQ5Zsovc+AUW5ubt37DFXXSrTAP+3UtY+Hhye/uIl165OfZy9aH3kxfvSEOVT3yLl7cvkxk+ZR4uGzxkQL/NODp2/T6gfhe3u9+56ff4AYJNugcfMifgGK4ZwNm7T0LeyXqHp+v2btBtVq1JVLNmraKkf/1PjpCzd397adussV6WPSuWrbsVuiBf7p/Sf/ENOzRl58QCeKchet/EjeoIWCf2q/sH+qTAWggODGOawrYH0QezYF/FPIGtK3568AIQqphRCFbFz6hmga/FNIJfinWUrtn5699mkhH98mzVonPPtz2OipVGDN5kPqijn6p/OXbaQCa7ccFik8b+mH2yN50OiAYcEiK+7ztNJlyhcrXjL+6T8TLfBPSVWr12nXuYeff4A8Q+u4KaGKAz51+RM3N7eOXXolqvzTxm+86RdQVMyyev1Bio9PYeGf7ou8QYWXrNrGq/L7o6iil5c3z7jKWvjBFsqdszg8MSf/1LD/gru7x/J1u0Qu7yhkzgqRYrngn9ov8E+BpsDDAnqB2LMp4J9C1pC+PX8FCFFILYQoZOPSN0TT4J9CKsE/zVIK//TB09/r1m/i41P4/PXPE02vNipXoYp/QLHLt79UVMzRP73z6BeqW8QvYOGKrScuJaz8MKJY8ZK16jbkEaktWndydXUd/n7IoTN3tx+81KR5G9oaleS6lvinM0LXuJg4ePq2SIy5/13JwNK+hf3mL9sYefHBms2HSpcp7+VdiJYTVf5pyJwVtNq158C9x2PWG47VrN2APqnwT09deUi5jZq2mrf0Qzotsn+668hVN3f38hWrhW87fvzC/VkL1noX8qFV+siJOfmn9x6nvla6bLESpVZs2Ee72Hnocr2Gb3h5eZ+59kiUt1zwT+0X+KdAU+BhAb1A7NkU8E8ha0jfnr8ChCikFkIUsnHpG6Jp8E8hleCfZimFfzp28nyXzHGUrF1Hrrq6ujZv1UHxUHyO/inpYuwzqsguJ9GmwzvCh73z6JfeA0Z6eHhyVuDrZZat3SkqWuKfRsd9Q9WrVKstJ5LORT9mN5apVbfhgVO3OEvhn9599Otb3frRp6NE2tTg9yaNGj9b+KfxX/zRrnMP3sjpq0myf5poMknLV6zKuW7u7h3e6ilnuWTvnyaantmnD8h1iTLlKvFbs/Ig+Kf2C/xToCnwsIBeIPZsCvinkDWkb89fAUIUUgshCtm49A3RNPinkErwT/XSNeM3R8/HxSb8oM66nfScss5FP47/4g91bn5EOz181qgeNqtWdNy3R87dy/LwSDfiv88ui3T++ufZfbQcRfulujzON8+Cf2q/wD8FmgIPC+gFYs+mgH8KWUP69vwVIEQhtRCikI1L3xBNg38KqQT/FHI8wT+1X+CfAk2BhwX0ArFnU8A/hawhfXv+ChCikFoIUcjGpW+IpsE/hVSCfwo5nuzUP42JiWH3EAAt2gnJycnTpk1T7gk4JdRJUcaHlvznTz9+e/AANTacWStmz6Izf3PjBnWWU4mancr40AP4p5A1pG/PXwFCFFILIQrZuPQN0TT4p5BK8E8hx5Od+qfcbAAgWBt7i6+QADDK+NCSNFP708kVt2XTzY0bvti5Q53lbFLGhx7AP4WsIX17/goQopBaCFHIxqVviHL7Ff4pJAv+KeR4smv/9IuHl9L/+QhyWlEABGvpn54/uVu9U8ippJd/ivYnBP8Uci7p2/NXgBCF1EKIQjYufUOUGy5ov0Ky4J9Cjif4p5D9Cv4ppLXgn0J6Cf4p5FzSt+evACEKqYUQhWxc+oYoN1zQfoVkwT+FHE/wTyH7FfxTSGvBP4X0EvxTyLmkb89fAUIUUgshCtm49A1Rbrig/QrJgn8KOZ7gn0L2K/inkNaCfwrpJfinkHNJ356/AoQopBZCFLJx6Rui3HBB+xWSBf8UcjzBP4XsV/BPIa0F/xTSS/BPIeeSvj1/BQhRSC2EKGTj0jdEueGC9iskC/4p5HiCfwrZr+CfQloL/imkl+CfQs4lfXv+ChCikFoIUcjGpW+IcsMF7VdIFvxTyPEE/xSyX8E/hbQW/FNIL8E/hZxL+vb8FSBEIbUQopCNS98Q5YYL2q+QLPinkOMJ/ilkv4J/Cmkt+KeQXoJ/CjmX9O35K0CIQmohRCEbl74hyg0XtF8hWfBPIccT/FPIfgX/FNJa8E8hvQT/FHIu6dvzV4AQhdRCiEI2Ln1DlBsuaL9CsuCfQo4n+KeQ/Qr+KaS14J9Cegn+KeRc0rfnrwAhCqmFEIVsXPqGKDdc0H6FZME/hRxP8E8h+xX8U0hrwT+F9BL8U8i5pG/PXwFCFFILIQrZuPQNUW64oP0KyYJ/Cjme4J9C9iv4p5DWgn8K6SX4p5BzSd+evwKEKKQWQhSycekbotxwQfsVkgX/FHI8wT+F7FfwTyGtBf8U0kvwTyHnkr49fwUIUUgthChk49I3RLnhgvYrJAv+KeR4gn8K2a/gn0JaC/4ppJfgn0LOJX17/goQopBaCFHIxqVviHLDBe1XSBb8U8jxBP8Usl/BP4W0FvxTSC/BP4WcS/r2/BUgRCG1EKKQjUvfEOWGC9qvkCz4p5DjCf4pZL+CfwppLfinkF6Cfwo5l/Tt+StAiEJqIUQhG5e+IcoNF7RfIVnwTyHHE/xTyH4F/xTSWoooJA8AAH6rSURBVPBPIb0E/xRyLunb81eAEIXUQohCNi59Q5QbLo7dfv382rWECxforzoLylLwTyHHE/xTyH4F/xTSWvBPIb3k+P7p/mOXqPMPQazwzbt17PkrQIhCaiFEIRuXviHKDZcfzp7+5dZNR9X9Eye4Y0AsmTt31aJFx7ZuubJvH6V/F31NXR6ieIB/CjmY4J9C9iv4p5DW4jaSMj60hNuf8E8hx/dPAVCjV89fAUIUZAdCFNg4eoWoaLg4sL7YuUN5uiWmTpy4YvasLQsWHFy+jEqqqzut0uCfQg4k+KeQ/Qr+KaS1uEWkjA8t4fYn/FOI25zK+NCDgvdPk5OTw0Em69atmzZtmuiAmSEsLExZ2bEwGAypqanKcNEDhKjM7NmzlUnOCkLUmixcuFB5EcwKxKeMjiH6/35Lpcarw2tuTvdrKnB9T0Y7HmI9j75GsaEMFzsE/inEgn8K2a/gn0Jai9tCyvjQEjbOfoJ/6vRyZP8UqImIiBC9rywJDQ1V1gFAe5KSkij89HJkgDNDUae8DqqIiYlRVgNAA1JSUoxGY2Rk5MyZM5VRKEEFXr58qawMHAL4pxAL/ilkv4J/Cmktbg4p40NL2DiDfwrBP3U6qN8l+mBq9HokEzg57OzDpQK6YDAYlJdCCaPRqKwAQAEhDFM2HXKEilEV5VaAAwH/FGLBP4XsV/BPIa3FjSJlfGgJG2fwTyH4p84I9dZEZ0zB7du3laUB0JiXL19y+IWFhSnzANCeY8eOvXoh/DchISHwqkDBkpycbMYwnTZtGqVT//DSpYzOpwxFI6x8ZwD+KcSCfwrZr+CfQlqLm0bK+NASNs7gn0LwT52UpKQk6oyJjplMaGgo3ZzwJDWwGrKhD7sKWA0KtsjIyOwmhoZ5CgqE5OTkmJiYiIiIsLAwZZBJhindlOXbrvivEoMH9p0H+KcQC/4pZL+CfwppLW4dKeNDS9g4g38KwT91XlJSUhQWquIdUwaDgXp0ymoAFDTyOKzIyEhlNgAFysuXL41Go2xmhYaGrl69WqwGm4ZCw64CeSNvhqkaCstgPLDvfMA/hVjwTyH7FfxTSGtxm0oZH1rCxhn8Uwj+qVNDvTLRwQsJCfm/TGdB9rOosxcZGWm+mwdAnlG8vQdvMAPaQVe8iIiILP9LJMchXQBhngLLKSjDVAFtEFNCOyHwTyEW/FPIfgX/FNJa3MRSxoeWsHEG/xSCf+rsvHz5krt8inF/1M1TPNlKd0FMvgYKnJiYGBFjDEY9g4KFrnIUZooBp5SiMLO41xcRESEnAqDGvGFK0ZU3wxSA/4N/CmUK/ilkv4J/CmktbnEp40NL2DiDfwrBPwUZ5gL1A7Pr5mU5HBWPE4KCgp9RlYGBBQqK5ORkCidFdFGispwJfquPMhUACwxTg8FAnToqhpHLIJ/AP4VY8E8h+xX8U0hrcQNMGR9awsYZ/FMI/imwiNTUVLpjyVYXdSONRiP6iiA/pKSkiIgShISEIK5AfqDrVUxMjHy94gGniCtgITBMgV6wOxC+eXfU9U8hZ1bowmXB1nUHCgT4p1A6/FNIe3F7TBkfWsLGGfxTCP4pyB1JSUnUbxTdyGnTplEPE8NRQd6IjIwUsSSDmSJA3sAFCuSBly9fJicnU6+MggeGKdAXdgcAYJTxYfPAP4XS4Z9C2sv6V0g2zuCfQvBPQV5QD++iPieGd4HcIk+wK2MwGJRFAcgeDJAHuUI2TNVTiATDMAX6QVezcBAePnv27HXr1ilTnQx7bAuFwz+F4J9C2otba8r40BI2zuCfQvBPQb7I1fSCAMhQnMiRoyC7CXkBkDEajYoBp5igGaiBYQqAfREeHo7J0O0R+KdQOvxTSHtx400ZH1rCxhn8Uwj+KSgA+PXWmG0Q5BluD9FfZQYAWZGamhoZGSmPX6ZmOqZ9AAIYpgDYNTyTRlJSkjID2DbwT6F0+KeQ9uK2nDI+tISNM/inEPxTUJCkpKRERETIpgZ1UNH8BTkC/xRYiNFo5O4ZwwNOMVoZ5GiYhoWF0e0pJiYGhikAtg//bENCQnB5ty8Kyj/939+Toi/sevlLvDorR3356cXPHpxWp9uyfku58+DmkbTv76qzctSThLOP79vW57UL/zT+1tEfkmPU6Tkq9bvbt6/uV6fbsv477SEF2HdfXFNn5aiUL65R3X/99ok6S0fxPUIZH1rCxln+/dNf4+Pvnzp1df/+b2/dUuea15fXr1+JiHhw+rQ6y7H1IiHh+qFDn12+zKt0Bu6dPKkuZh3BPwUFD3VNjUaj/BYO6s3SDQ+NYJAd8E+BefC/GaAgV4apsjIAwLYRP2QtLBigHQXln4avnOXi4vLVZx+rs8zrxbe3K5Qr/d7QnuosW9bpIxvp856P3KLOUuubJ1emTRouVps1rVejWkV1MR1l+/7pveuHPDzc925brs4yr//5x8Ne3dtTjKmzbFm/pdyhAJs59T11llp//Bw3b8b7KZlm69zpo6nuj3+7ri6po/gGoYwPLWHjLJ/+6YmtW8uVLu1iwtvLa/KI4S8SEtTF1PrBaBzSs4erqyvXrVWlyvVDh9TFHFXf3LxJn5pOF682qVu3aoUK6mLWEfxToCGwPICFwD8FWYJ/xgABfekwTAFwEuSfNv2oldnAVikQ/3TbxoVubm558E9/SI5p1KAWVXRs/7RT++ayf7dwzrgZU0aoi+koG/dP70YfKF7Mn054bv3T/3qRMHJYL6ro2P7pB4unyL++w3vXDOz3dt4GR2snvjso40NL2DjLj39qPHHCx9u7cZ06V/fvfxgVNeP9DGN61pj31SXVGti9G10VV8yamXThwvmdOyuXL1/U3z85Olpd0iGl8E9njx0zafgwdTHrCP4psAZ45BaYB/4pUID/vgC6R9A3TpcFun3IkSCAYQqAQ6J+vSTeCmgv5NM//ebJlYH93qZ+ctEAv1z5p//7e9KBXSsDSxXnio7tn3Zs94p/aoOyWf/0j5/jlsyf4OnpwXGSK//0bvSBRg1qubm5+fsVtvHzr1au/NOwRZNz9evTRXxrUMaHlrBxlh//dFT/fq6uro8//likNKlbt1hAgLqkQk+vXqXAG9G3j0iJ3LqFvqO18+aqCzukFP6pvoJ/CqwHXvkCsgP+KWDwMjpnBoYpAOD/svJP6bePu4BdkE//tFP75n5FfDeumbN43njZwfmffzyklOmTh/+WckcUDl85a9qk4b98fZOWb1/dT+XbtW76t0cXLPFP//XbJ3sMy/r27NShbbOxI/t9cjdSzo06sXXIgHdat2rcp2dHKiZmfqSFkInDbl2JoN0NG9S9zZtNRgzpce/6Ic7dtz2MjkcxZ+vmdfMWzQ3m5f96kbBt48Ke3dpxxWtRO0Ux2T99dO8k7UWewjXli2uUcuPSXlpeMHtsuTKv0VmilEunDZTy4erZK5dOFYV//vuNJfMndO7Qon2bN6ZOGPrFJ+dFFm2fqv/5/P4Hi6dQAdKa5dP/4/kDUeBp4rkZU0Zw1pzpo/7+OI/fo836p/TB3dzcxo3qT6dC4Z/S10enNP7WUZFy5ugmShHfr4eHe6UKZa6e39m2dRNL/FMqSXFIMTn43a70/cpZ9BVPHDuIttOlUys6JDmq6duko/rmyZUp44dQ3d49OhzZt5azKOroeBRz3V48ZaBE+tJ5NbvQlf1T+sapytljm+XtzJvx/q6tS2mB9t6iWQMqPGp47y3r56ebwobK//7jPS5pJoyfJJylkj8kxxzYtZIK0PHTL0LMA5Buml6DYrVX9/aUNWZkP/opyceQK/GtQRkfWsLGWX7809vHju1bs1pO6diypZen52+JiakJCXODx00cNvSbmzdF7gczZ0wYNvTL69dT7tw5snHj/VOnRNbV/RlXvEWTJ6n3Qlo5a+ZHy5YmXbgwesC7rRo3Hti9G+2a0k8ZDD07dmzfvPmsMe9/f+eOKP/LgweG5cv6delChSl36sj3Hl28yFmUPn7okBuHD4vChz/8kFKuHTiQ5X43L1l8NzJycI/ubzZpMqx3L7ki6fn9++ELQt9p25Z2RGXObt+u2MKxzZve7dq1ZaNGPTp02Lp0ya/x8Zyu8E9pR4unTBbL9GE/vXRp3OBBtNPu7dvvWb1K3ua3t24tnDypU6tWdLbXzZv7yfnzdPz5mUMW/inQAaPRaDAYRLOYh6NicIEzA/8UUIc5IiJCXBYIWoVN5tjAMAUAqFH7pwQ1FJXlgO2RT//0aMRadoKWzJ8g+6ek/TtXsKfDqwd2raRVMQ3ol59ejDqxlZdz9E//X2pi+zZvULG2rZsEj363auXybm5u547/206aNG4wZdWqUXn0iD7N36hPy53aN/+vFwmU9fKXeFrt2K65p6dHh7bNevfo4OPjTcuJd46nZx7h8QPrxY5Sv7vt5eX5/nt9afmfP91r1rQe73TksF5VKpXLsD8yrVXZP1WPRf3kbiSlfLh6Ni0PH9y9VMlitN9e3duzsybPf/r4/unAUsULFfKiYxv8btfixfxpWZyZudNHB/gXofLly77ev3fnurWr0mb79eok6vr7Fa5csSydEzrPRQP8aPXZw7/sV8tls/7p9Ut7Ht07yQsur/qnTxPP+foUou/9P3/NcMB5tWnjOhQtXGDH5kWcZYl/umD2WI6icaP6v9myES3PChnJWYf2rKaYeS2w+LBB3bt3bevh4U5bE1Y1fTuNGtQqUTygQb0aA/p2ef21ElSXfcxvnlyhQJ04dpC8oyaN6lAA87KZ0JX90yzHolJgUETRwprl02kLVKBzhxb0KdJfnf/UfBhfOm2g1bc6tqTI6dmtHf/KygYF8j8V/vg5rl6darSjEUN6TBgzkA7b1dWVfvLyYVguvi8o40NL2DjLj3+q0IXduwt5efXt0oVXt38QRqdrWO9evLrDtDph2FB1xZ/i4np06ODh7m48cUKdm2Ya1lq/Ro2i/v7NGzTo3r497aWIr2/oxAmFfX3fadu2Wf2M2Hi7TRsu/CIhoVXjxhRanVq1Gv1u/44tW9L3UiwggF9vFX/mjG+hQtUrVfo5Lk6sNqpd+5cHD7Lcb9UKFQr7+NBGJg0fVrFsWU8Pj4Ph4Zz7/Z07VIB2Tbsb2qsn5dLynHFjRfUxgwZSCu2LTkLTehlh1q5Zs+f376ep/FN5/lNablCzJh1wverV+7z1VmCJjJ+MGJn7VWwsbZA+HX3e/m+/7Ve4cI3KGeF9YutW+chzJfinQDeo50x3QXmgGXWVjUYjhhg4IfBPnRa6DigGnNJ1AANOHZUcDVNKh2EKgJNDVwDlpcEEpnCxffLpnwqp/VMSP9r/8ZltX3560a+Ib+OGtdkbUsglJ/903QczqMzmdfN49c/n92tWr1S+7Ov/+3vSmaObKGv44O5i4B4/y7w0dEJ6pn/q4eEuxs3xuNexI/ulm4b1+fsVZhOKZfhwAeXyu9qnTRpOyzs2L+Is2lS3t9tQyvVLe9Jz45+mq57fl/3ThvVr+vh4i+G0331xjUoWDfDjySvZCKMjZB/wf/7x8K2OGV4Jv5Z9wpiB3t5eYpgh73ThnHFiR5bLZv1TIbV/mp75fc2fOYbiis4kfZsUaeq6Ofqn92OP0FmlcBXe64ghPWjLf3t04ce/XacvqF6dar9+c4uz6Ejc3d3atW7Kq+xOrl4+jVdffHs7sFTxalUq8Gqn9s1LligqNvt5/Bkq/MHiKemm0bIu2Yeu5f6pqCh+fbJ/aj6M2T+tVKGMeNkUT6V6aM9qWj62fx0tXz67nbP++dO9MkGBb7ZsJI4hV+KbgjI+tISNswLxT9fMndO4Th06G51btfru9m2R3rdLF0o8+dFHD6Oiivj6NqxVi91DoWfR0W+3aVOiWLHCPj6HNmxQb5nFNmXYjOm8ys6sb6FCCWfPckqPDh0ohR3SvatX0fKq2bNE9XXz5lLK5iWLeTU8dD6tznh/NB1M/Ro1/AoXpsNT7zQtc7/TR4/iVdp+tYoVSxYv/oPRSKsThg2l3A8XLuDcn+LiurRuTSlRu3almYa10vKg7t3EmNMFkyZSyrzxwWk5+aeUtTRkKq9+FRtLe6xcvjyvjhs8iHKPbNzIq5+cP1/UP2PuY/inwL6hBrFiOCr1nzEc1amAf+qE4IfvDFhimEZGRhqNRnz1AACGmwRq6BqCf63ZOJr6p6nf3S4bFFipQplWLRr6FfFNTopSV0y3wD9tWL9mxfJBcsqtKxGH9675z1/j+/bs5OHh/vyrWJH132kPqTD7ZeyfdmrfXK5boniASBk7sp+Xl+eLb2/zaotmDdj5+t/fk0qWKFq/bg25ojzVQIH4p4/unaRiUycMlfeya+tSl0yjkI0w9nNZW9ZnOCOcMmX8EFrevunfoyzTTQ6XvCnLZaf+KYkHhA7qn+HUZzc0Mkf/dPrkDJNRjl5api/i+2fRm9fNo6xThz+Uyw8b1F2Up2/Tx8dbOKSkfr0yYpKXD+1ZLcfG/Jlj3Nzcvn16lZbNh26B+Kc5hjH7p+znsj6NOyVSTh7eQMvvv9eXfsicm+cAS7dz/3Te+ODhfXpXr1SpkJdX6MQJIv3r2NigwMAKQUHNGzQo4uubeO6couK9kyeH9OzRo2NHL0/POtWqfXL+vHrjaSZL0dvLS3ivSRcyviMx0JW0bFoIpfDw1SdXrhzZuPHHe/dE7o3DhymXyoiUt9u08XB372eyd/e++nS8LNqvf5Ei39+9K1I2L1nsYrIvf0tMLF60aN3q1eXydPyUS5+IlulD0S7+FhMjcl8kJJQPCipXunRaTv6pj7e3PB62p2lTtJCakEA7bdmokchKM518F/inwDFQD0PDvIfOA/xT5wEDzx0bGKYAgHyi9k/pNkGJGJZu+2jqn5JiLu52dXV1yRzRlqWEm5OdfHy8e/fooE4n1axeqVKFMorEPj070jb/4/kD9k/5eXyhCuVKi8GDxpiDVGDbxoXpmb4Sz0z605c3XKTJB4SKFfVv1rReegH5p0cj1lKxiB0fyLtIuneCEudOH52eaYTxaFPW3m3LKYVnVn2aeK5E8QBaLezr071rW8OHC2QzLleyX//057/fCCxV3CVzTHGWytE/fbvzm0UD/NTppPHvD6CNf/Pkipy4aW3GcD+ezZa+zSqVysm5FMyUy8sUgbTlgf3eTjeZ8uXLvv5Wx5acZT50C8Q/zTGM2T89sGulyKKNuEjDt5s0yhh06e7u1rJ5g+ULJz1JOKvYlOXiW4MyPrSEjbMC8U9Zv8bH9+7cmU5I5NYtIvH8zp18idu1coW6ilDMoUNenp6N69RRZ6WZLMXyQUFi9Ytr12iDU94bIVJWzppJKbdMk6KyLu3ZEzZj+uh3+7dq3Liwjw/lLpk6ReQmR0eXLJ7xu3ivX195RwrRfpvWqyen8DyttKln0dEu0uwEQkX9/akWLVSrWLGCdMwsHif747175v3TimXLyrWG9MwY7p1meu8WLbw/cICce2hDho8P/xQ4FJgG0QmBf+oMGI1G7lkxmPjYMaBvkL5ZGKYAgIKCH00IDQ3dvXv3zJkzg9E8sB+09k+NMQfd3d1cpKfv1cro5Gfvn/532kMqMKh/hgOlVqUKZerVqaZI5OGBfz6/z/5p8Oh35dwK5Uq3bd1ErNaqUZkfSV48b7ybmxu/POebJ1eo4pTxQxRbLhsU2LRxnfSc/NPEO8ddLPBPI3Z8QMVOHt4g74Jt3NnTRqW/+iA2i/1Tfvia9Pyr2DXLp9Pxe3p6ULqvT6ETh17ZmoWyX//0t5Q79KW4SNPCqpWjf9q6VePXXyuhTieNGt6bNi5GKLN4jPCFkx+lvzobA0v2T9NNDqyPj/fvP96Lubib0sXbpcyHbo7+qTz1RHb+aY5hzP6pOKT0TP+Ufs68+l8vEvZtD+vZrZ1fEV9Kd3V15cjMg7iFqYwPLWHjrAD9U1LcyYwx46MHvCtSru7f7+6WcYlbM3eOurws9l6To6PVWbK9mJbpn04bNVKkyP7pt7du8YyoRf39WzVuHDxk8ApTruyffnPzZlBgxu+iZ8eO6t0J0X47tmwpp9w+doxqzZ8w/tNLl2hh3OBBiiq02Ua1a9NChaCg2lWrKnIHdu9GtX4wGs37p/KHTZP8Ux54O37oEDn3xNatLvBPgUPCr+EOCwsTnXAejpqamqosCuwf+KcODP1mIyMjZWeNmtRGo1FZDtgJbJjSdyq74TIwTAEA+SEpKUlcPWiZLyz4P7pdoKl/+sfPcVUrl69YPuitji19fLzlN9TLcjHrn5JKlija5s2/HM9005PvOzYvev5VbMvmDQL8i/zv70ly7pstG/kV8U3PfH7fvH+6Nmw6zyhapVK5tzu/yYn/8fyBu7tbz27t5Ir/9SLBzc2txzsZibJnevbYZpdXH/GOvrDLxQL/9Mq5HVRsw6p/F2Ndi9pJieErZ6Vb4J/++fw+L/z+471De1b7+xUuExQob81C2a9/Oqj/256eHuxy7jEsU1dMt8A/7d+7M21Enpz3Hz8YN62d+2ncqdBZGe+V4heOCS2cM44SE24fS7fAP70fe4RWD+5eNXpEn6IBfvxqJpL50JU9UzoYl1fnefjXb59QKObon+YYxjn6pyLAaI8U1fXqVKPcvL2jjO8LyvjQEjbO8uOfzhsfzLN5CrGrOLhHd179/u7dSuXKlQ8K6tCihY+3d9zJk5wec+jQsN697kZGynUpheomXcjieHLln/J2ti5d8ltiIude3rcv41uT/NN+Xbp4eniIkvK+ZNF+a1SuLKcc2ZhxZfto2dIf791zd3N7p21bOff5/fsUPF1Nic3q1/cvUkQcA6tFw4ZFfH3Tcnp+Pzv/9Oe4OC9PT/GmLNZa0+yu8E+BI0Nt6IiICNl8MRgMeIeAgwH/1PF4+fJllgNO8S8QuwOGKQBAR/iZJEx+ahdo6p+OGdnP1dU1+sKu77645u9XuGH9mnl7f9SAvl28vDy/fxYtUsaO7Ofh4f7i29uL542n6scPrBdZn8efoR5+354ZoxEt8U9//Nt12hR7Xsf2rxPpbd5s4u3txVNVsnZsXuSSOZBW9k95XOH6FTNFyXkz3neR/NO3OrYsV+Y1kSsct99/vFfY16denWr/84+HInfwu12pLr903rx/2q510yqVysl12acWq5bLTv3Tg7szXqRDMfDfaQ/faFLXr4hv3t4fxe+hijwYLlJ43tKzxzbzlztx7CCRRTFMWytVshjtNN0C/5RUt3bVXt3bFw3wmzBmoEg0H7qyf8puafeubUXJj89sczG9WIxXVy6dSqvis8thYz6Mzfun9Jd+v19/flnk8o7ibhwWKZaL25/K+NASNs7y4592ad3a28vr88uXRcoHMzPeZSdeqTSibx86Red27Pjs8mW/woXr16jBc5heP3SIio0ZNFBU/O727dKBga+XKqUwHFm58k8b1a5d2ORRCoWMfI9yF06exKs7V2QMbJ8bPO5FQkLjOnWK+Pqaf3/UzaNHRUqPjh3d3dyeXr1Ky60aN6aP//jjj0UufXCXzJG2tH1ajli7RuTeP3Uqw5o3jXjNm39K6t6+vaeHh5hJls5n9UqVXOCfAmeAvRjFcFS6icKLcQzgnzoS+J+HvQPDFABgO1ALkJt/9BcWqo2jnX/Krxcf//4AXt25JeO1JHOmZ/Hwr0tO/mnSvRPe3l41q1c6H7nl07hTq5aFUC99cvBgyvrl65tBpUv5FfE1fLjg0b2Txw+sr1CudKFCXuw/WuKfknq8046KFSvqL17ERIr9eK+7u1v1qhVOH9lIB/Dh6tk+Pt60+sfPcemv+qe0l+LF/EuWKHpw96q70QcWzhnHM3IK/3Rgv7dpUyuWTOH3PsmOGyVSyXe6tKbd3Y89MnFsxrunhwx4h3PN+6cfbQil5aEDu8XdOPzZg9PhK2fRXkYO6yUKWy579E+/eXIlwL9IvTrV+d1Nj++f9vLybNm8AduasnL0T3mgNG2NopS2c3jvmlIlizVuWJvt/rc6tnR1dZ059b34W0ejL+xq17opHQmV5LqW+Kf01biYoK9YJJoPXcUz+13fepNinqLFGHOQdv1aYPGiAX7CP922cSEVHj2iD1vActiYD2Pz/ulDY6Snp0eTRnWunNvxNPHciUMbaL/VqlT412+fiPKWi5ujyvjQEjbO8uOfXt2/39PDo36NGud27Ig7eXJpyFRvL6+61av/HBeXlvkC+tHv9ufCGxdlfAshI9/j1XfatqWvbNHkSfdPnaLqTevVo9w92bzKKVf+6fsDM+bkXTxl8rPo6IdRUXPGjfUtVEiU//TSJf8iRepUq8YvaLp38qSXp2ez+vVfJCRkuV+qWD4oiD7LzaNHg4cMdpFMz4u7d7u7uVUpX/7Qhg13jh+nY/Dx9qZVft/Ul9evv16qVBFf3/DQ+XcjIyPWrilXunQhLy8edZtn/zT+zBnaZsnixenUrZ03t2GtWrRNyj350UdylVwJ/imwM6i7rngW2GAw4Flgewf+qQOAf3LYL+YNU7reUjp9lfJDtQAAYDXoysMNP2ryKfOALaGRf/rTlzdKlSxWoVxp+YXdndo3d3Nz43cfyXLJyT9NN9lnNapVdDHh6ekxffJw8cbz5KQotrSYxg1ri/FxFvqnpw5n+CCTxmUYsrIunTZUq1KBN+vu7tanZ0dhZSrmPKWF0q+X5JJUhV9LJfzT6Au7igb4uWTO4qpw3NavmFmsqD/XLVLYd/a0UWKUrnn/lLRobjBV4bp0Wug0igeucyW780//5x8P6Uv08HCXn6znx9iXL5ykqJujf5pucmMpPvlMEt27tuWZcNNN7ur77/XlGWaJskGB+7aHiYqW+Kc///0GVa9Tq4qcmG42dBX+6bOH5xs1qMXFKJboAFq1aCj80x+SY/jXwbO4KsLGTBib909JkQfDy5d9nesSbd5sQscsCudK3EBVxoeWsHGWH/+UdDA8vLRpIlEiY86ETp2+vH6d0p9FR5coVqxc6dLf37kjCrdr1ozKXNi9O800S2nfLl3cTFOjErSR7MzTNJWlaN4//To2tkOLFpnfiUurxo3vHD9eqVy5hrVqpSYk0KqHu7s8pHTBpIlULHTihCz3SxUnDR/Gx+lbqNDssWNSJaf1xNatlcuX5x25u7n16NCBjk3kJp4792aTJuJI6ACiDx7krDz7pyT6mO2bNy/s4+NfpMiw3r14zG/Url1ylVwJ/imwV/BosCMB/9Su4Xe+yf/VoFUMOLVlLDdMcVEFANgCwkKl+4syD9gMBeWfWkdffnrx0b2T//H8gTrrpy9vJNw+JjyvAhTt9JO7kanfvfIGIbX+O+3h4/unnyaeU8xoyfp/qYk/JMcIz1ehf/32CdUlZfnRzOs/f41/9vD8Q2Pk7z/+ZVXnVrbvn1pHFEXZfdd0eikrOSlKnjChQGR56P798aWkeyfEDKqyKOp+/Nt1HlWapSwMY7Xo83779GrineN0nOpcy8XtVWV8aAkbZ/n0T0mpCQlxJ0/GHDr0VWysOte8/n7jRvTBg/dPncrysf386Fl09M2jR59l9TYqyyWszOTo6DvHj/PAUrUeRkXdOnbs62w+Ph1D7JEj8iwH+dHjjz/mkbNC7J/ey5xbNg+CfwrsG7yaxjGAf2qP8EveQkNDxa+PX/KG5yttEBimAAB7Bxaq7WNf/imkkeCfQlqLm6/K+NASNs7y7586sNRDQXVXq8aNgwIDeYaENNP8pw1q1izq7/9rfLy6sIWCfwocBOr2GwwG2Q6g5jUeNbUX4J/aFzzgVPzcgk29Wbwc2aagryMmJoa+F3lGBQEMUwCAPUJXNr6IwUK1TeCfQunwTyHtxTcCZXxoCRtn8E/NyAb9U375Vb3q1ecGj5s9dkztqlXd3Ny2fxCmLmm54J8ChyI1NZXurPKAuLCwMKPRiAFxNg78U7sAvy9bBoYpAMAZoJsOX9YMBgPuPrYG/FMoHf4ppL34LqCMDy1h4wz+qRnNeH908JDB6nR9dWb79u7t29eoXLl6pUp93nrr47171WVyJfinwDHBcFT7Av6pjYMflA0CwxQA4JwIC5WufrgT2RTwT6F0+KeQ9uJbgDI+tISNM/inEPxT4MhgfkZ7Af6pbYIBpzYFDFMAAGBSUlL43kSXPryu0HaAfwqlwz+FtBc3fZXxoSVsnME/heCfAqcA0zXaOPBPbQ2j0Si/aIg6qJGRkXDlrIx5wzQ0NNRgMNCvhorB0QYAOBt03RP3KbQfbAT4p1A6/FNIe/GVXxkfWsLGGfxTCP4pcCJ4OKrsRISGhtKdGK6Q7sA/tRHotxAZGckvOGao+Ws0GpXlgDbAMAUAgFxB9yy+QmI6VFsA/imUDv8U0l582VfGh5awcQb/FIJ/CpyRlJSUiIgI2SSiZjee/9IR+Kf6Qn1Oo9Go+NcCBpxqDZ325ORkCnsYpgAAkGfo/sUtOjzLrzvwT6F0+KeQ9uJ2sjI+tISNM/inEPxT4Lxk6RlhOKouwD/VC/wvwZoIw5ROsjyrrACGKQAA5AG6l4nmHF1F0ZDTC/inUDr8U0h78dVeGR9awsYZ/FMI/ikAeGZZf+CfWhn888A6wDAFAACrQddSMRA1JiZGmQ20B/4plA7/FNJe3IpWxoeWsHEG/xSCfwrAX+CdOXoB/9RqZPkuNQw4LShgmAIAgI5Qm0005GghJSVFWQJoCfxTKB3+KaS9+CKvjA8tYeMM/ikE/xQAJTwcVfY+eDgqzA7tgH+qNfzyNDmqaZlSENX5JEfDNCwsLCIiAoYpAABYDbq7iYeK6AqMa6/VgH8KpcM/hbQXX96V8aElbJzBP4XgnwKQLUlJSQaD4d8uiGk4KrXCMZZBC+CfageFsTzglMM4OTlZWQ5YhoWGKfXecZIBAEAv6FotmnB046OLNlxUKwD/FEqHfwppL762K+NDS9g4g38KwT8FIAdSU1Ppbi0bJWFhYRiOWrDAPy1wELcFBQxTAACwU+iyLB7nZxdVWQIUKPBPoXT4p5D24qu6Mj60hI0z+KcQ/FMALAXj+LQD/mkBgnHT+SQ1NZXOIUUjNf3l18oJYJgCAIAdQddq8d8vWsALQrUD/imUDv8U0l58PVfGh5awcQb/FIJ/CkDuwDySWgD/NP9g3t48A8MUAAAcHrohwkXVGvinUDr8U0h78ZVcGR9awsYZ/FMI/ikAeSTL95jDXskb8E/zA3UCxfOJwaYBp5GRkampqcpyIBMYpgAA4JzIr5YKDQ2NwryoBQr8Uygd/imkvfgarowPLWHjDP4pBP8UgHxBzW6j0RgWFvZv3yWzOQ73KlfAP80DPOBUtv94wKmyHIBhCgAAIBNuuYmxqDzLDZptBQL8Uygd/imkvfjqrYwPLWHjDP4pBP8UgIIhJSWF2t+yNWMwGJKSkpTlQFbAP7UcWPaWkKNhSumRkZF0JjEzLAAAOCeKpzfwFFH+gX8KpcM/hbQXX7SV8aElbJzBP4XgnwJQkGTpbeFh6hyBf2oJ8OjNAMMUAABAHkhOTpZfushPcuCh/rwB/xRKh38KaS++XCvjQ0vYOIN/CsE/BUAT8Gx1roB/aoYsTXkMOE1JSYFhCgAAoECgW6o8qT0/1I/bR26Bfwqlwz+FtBdfqJXxoSVsnME/heCfAqAteLePJcA/zRK8o0yG+rH0a6Kfj/yDkoFhCgAAID+8fPkyJiZG/oclLWM4quXAP4XS4Z9C2ouvz8r40BI2zuCfQvBPAbAGPBxVvKwALXIF8E9luP8mRwstU4qzRQsMUwAAALqgnjDHmf9/aTnwT6F0+KeQ9uLLsjI+tISNM/inEPxTAKxKUlKSPM0WHhBj4J8yWYaH83TYYJgCAACwHbKcP4duQ7gHZQf8Uygd/imkvfiCrIwPLWHjDP4pBP8UAB1ITU2lFgCGowqc3D912ngwb5hOmzYNhikAAADdUU9qDyM1S+CfQunwTyHtxddhZXxoCRtn8E8h+KcA6AkmuGSc1j/NcsCpA/fHLDFMKQzotGCOYAAAALYG3Z4Uz/WzkYp7FgP/FEqHfwppL778KuNDS9g4g38KwT8FQH8w36Wz+afqkSzUyjQajcpy9g8MUwAAAI6H2kgNCwuj25kD/wfUEuCfQunwTyHtxVddZXxoCRtn8E8h+KcA2BA8HFXxvgJqoyvLORzO458ajUbZSaTv2sHGrVAMx8TEUNzCMAUAAODwqI3U0NBQJ2m8qYF/CqXDP4W0F19slfGhJWycwT+F4J8CYHNk+b4CajE4sN/k8P5pSkqKYsCpwWBwjAGnwjCVI1YAwxQAAIDDQy03tZEanHmvd57bH/xTKB3+KaS9+AKrjA8tYeMM/ikE/xQA2yUlJUXRFqeGuEOOaHBU/9QhrXAYpgAAAEB28H9MFbdIfrrf4Se4h38KpcM/hbQXX1eV8aElbJzBP4XgnwJg67AHJz8N7XgvK3A8/9SRvG8YpgAAAEBuoXsi3T3lt0TyTZNSKN0hZ0qFfwqlwz+FtBdfTpXxoSVsnDmwf/r5tWs/xsWp0yGF4J8CYDc48EuHHMY/5VeBKQacUop9GYvmDVP6RNT343E09vW5AAAAAOsjnu6X3xTK91NKdKQH/OGfQunwT/MhOnV/Po9Xp0MK8SVUGR9awsaZA/unq5cupVO6NDT0xK6dn1+7pi4AseCfAmB/ZPkOIrseyOAA/im/+0t8KcGmd3/Zy5N6lhum1AlUVgYAAACAZaSmplIrju6qiplS6f5LbTl791Lhn0Lp8E/zIb4ahM6buW/XhuiLB/BTyk58opTxoSVsnDm8fyoImTJlx4bwO6dPY1CqQvBPAbBXqIVNzQh5LAM1vqnlbY8Ol/36pzzgVP4WeMCpLX8LdGwwTAEAAAB9SUlJoXux/B9x+S5MWfbyX1gBf5Z9uzacP7kbclpRAARr6Z+uX7NMvVPHUOi8WYqrATF71rSwZQs2bViBX5YQnxllfGgJG2cpkcd/unjBIWX4IIsuIbNyQeiZLVs+OXpEXcsJBf8UALsnKSlJnl1r2rRpERER9jUc1R79Uzs67WyY0umlA1Y8PMjAMAUAAAB0hG/T4eHhinGpwSYfKjIy0i4mGVdM9gqcGQoGZXzkG6PRqNwNcGKU8aElv3/2GRtndq2kbYabGzewDi5fxtq1ePGK2bNmT5miPL8qtixYoN6mc0oZH3oA/xSAfJHlcFQbHwgpsCP/NMvzbGvDfmGYAgAAAHZKSkoKtSuyfEbExoemUhspGZgmdJo6dSr9ffz4sTLPadDC7uf2rYORkJBAv+ijR4/u2LFj9uzZip+8zJQpU6iYsr6zYuUxK+n/+td//vSjDSrt668e3zOybl66dObIEdbWDRvWrVxJWrZ4sTKScs8eg4G2r967c+r//VbwF7c8AP8UgIIh2Q4n4rQL/5QnLBNn1aYGnMIwBQAAABwPcX+396GpTgV9a+LLorYivh3A8M+ZOhTcYlf/gyQ7QkJCqAoa8M4DdTCFUxyVCV1MwjNR3w4sgfqDYgtis/wPOYV7wFDg0S0GVzDbBP4pAAXJS7ualzPKhv1TumfQnUO+S9Eth5o+ynLWJUfDlNpkMEwBAAAAR0IMTVXf+uWhqbjv6w41HeVvx3b+4w6sBn3jSUlJwvbKzvCidMqlMuxkKbNNwYNftAMgj56mL1rYl8LQtNxPl+H4YTiKGIo93peFVx4qKW/Wln0DwMA/BUAT6KJJF1P5nk3Na7qkKsvpSpRN+qfURQmX3upA51DHf8FZYpjSF22zz/QBAAAAoAChhgG7M3JbRcCdasqlxgwaBtaHmovKr8T0D3h8Fw4Jz1xBjXDqKdC3nGVDnaFcHt+Q3Q9TLkwl9ep3AAv5tyGanMxXY0YeKCp/oZZD3TqxBbFZjhmmwJ3N5Ez/NNwGxgkBS4B/CoCG0EWWLoXy/7Xo1k4XYhu5K0fZkn9qI44zDFMAAAAAWAi1XqhJwP12ZYvBhHgwhZo0Fo5IAvkhy+dhg2FP2DnyM/jhZscM8rPSUZnPR1vY5+IOSDisdv2Q53EWA0XZGWey7JfliDxQlLb2b0/U9KQgY2GEaAT3fxF1dgT8UwCsgY2YgwqibMA/1d1iph2JgSRZ3phhmAIAAADAEkSjwsw0i+GZz3vq3nV3SLIcgspQOxwn3C5IzhxXaOY/E8GZ1hibYskWPzGdJTyjsTIV5BthU7L3zdDlkQ3N7C6SOSIsUf7vFCOenU9Glw1oBvxTAKyKTT2cHqWrf6qXpywbpllOigTDFAAAAAAFgjAOwrP5N63wgDCJakFhkN47KggJCcmPvwY0gkcdRmWONMzyN8IIs4xtMuWGgLWwwkuWxEBRvioyevWXAZCBfwqADqTaxsuRovTwT63/ii0YpgAAAACwBfgxZDFjo7JFkkl4plWUq2eQAZP86itZiIkTJ8I81R3Ln8HnaShF/GvXRwCM/JIleaCoMDTNfFnmEVvgQfdMbl+yBIDtAP8UAD2hW5T8T3Iejmq1e0mUdf1Tuk0qJqXSaMIXGKYAAAAAsAvE+DvzjyoHZ47PEk8row1jBvWZpNOrLAS0JNnqz+ADBWKgqL2/ZAkA2wH+KQD6Q01nuvHIQzLpzkT3Ia1vP1FW8U/p0ykGnNKnK9gBpzkaptwyy+6dmwAAAAAANgKPBZO9pyzbNgzbT/K7xQuwfWW/JGcOQQ0JCblw4QIvw0LVCPbpojKfwTcTruGZ4xDxDH7e4H+3MNZ8yRJ8bQAY+KcA2BDUmFAMR6VGhnZ3rCiN/VPtPo7lhmmB7A4AAAAAQF+ES8XtH/NP1PIYMfHMLJsgTmWthoaGijlPqUHIpwUWaj4RE1DkGITiGXzY+jkibMosX7IUnteBoqK6/JIleaCo8jgAADkB/xQAm8MKAzaZKG38Uy2G08IwBQAAAABQwOPR2HZhwyXLZpKMeE8L+yk8EtDxplilzyW3CWGh5oHkzHHQHFqvxtFfKGaWcLxYygNZvmRJHiia4+80S/CSJQD0Bf4pALZLssYThkYVtH9agNO5Ui3ampm3K3C7AYYpAAAAAIACNlPYVxWujbItlRWyNSOGqjmGKQML1Qx4Bj9H9HrJklOdZABsHPinANg6L7N5YX3+27JRBeSf0pFQY0tuaVELgBoWynJmgWEKAAAAAKA1PGRVvFKGjRsLp00Ur47humLgm720zWCh/l9mAOAZfEYMFLXCS5bkgaIOeTIBcHjgnwJgN9ANnm7nsk1pMBjoZq8sZzFR+fNP6cZPzSm5YcEDTi00dmGYAgAAAADYDmKEncJFMjMaUYF4vlg8XMwbtJ22nLNZqMmvTu/w6tf1F+IZfPb4LGzM2yZZvmQpShooauF/CxTIL1mSB4qKfdlOkAMANAL+KQB2BruW8v+KqRFAN+88NHSi8uqf5s3JhWEKAAAAAGC/iMF67ByJmQEsN6SECSUcKPGcsnJnmuGoFqp4Bp+N7+xcb/4KxDP49tLqFjalPFA0/y9ZEgNF8ZIlAECOwD8FwF6h5o7iqXm68efqqfmoXPqnubVuzRum3HqLMjXdstsCAAAAAACwF4TBKsb9sTll5iFxNVxFGKy8wYJtK9q7hap4Bt+Mf01nnp1BG3wGXx4oyt91FF6yBACwYeCfAmD35Pkh+iiL/dNk05us5EYMraoHnMIwBQAAAAAA2SEsM36oPEoaQqhsOGaFGL7KvpgwxZS7sQA7slDF6bL9Z/DxkiUAgAMD/xQAByE19y9xisrJP83u1VXif9fJpn99wzAFAAAAAAD5Rxhwwn1j18zMEEuBeN+RhQaiDVqoKSkp/Hy6TT2DL4YVZ/eSpeyO0zzyQFGxWbxkCQBgs8A/BcDRoJaNwWAQTRNq0FD7Jst2VVT2/mmyacDpXw0cU8uSmzLUrKHlLP97DMMUAAAAAABoh2iORknPepvx78SozCjTVACKBqqOFqp4Bp8/hRmDODxzdk5+Bl+5oTwhPzuPlywBAIAlwD8FwDGhVhE1VuSmT1hYGLW65H/kRqn8U3WthQsX7jYBwxQAAAAAANgs4gVK7EhmZ6pSusFg4HGO/2cVC5UH1Ypn8LNsVDPs9kZZPIRWgbAprfySJQwUBQA4A/BPAXBwzAxHjZL8U0WxiRMnzpgxQ6wKYJgCAAAAAAB7QZiJ/Ly5umUbGRl54cIFXi0QCzUPz+AnZz8wUx4oKrxL+SVLeRsomuVLlvhIGLTzAQBAAfxTAJyCLGcy3bz5/7d3J3A3lO0Dxx/b8yB7Qij7msiupKKFiFTa0KpFSkqLVGQpilJUqDcULUKkhQqhErJT2R5tXml/27z1ts3/6tx/d7eZOXPmzJxzPMzv+7k+Po+577PMzD33XOc658wZe1XsLWXXUqki3dRbzRRMAQAAcKDbGvvGuuS3ZmVz0KBB6o+kSqiquKkLmh6lzPuN7+Bv2LBBlynNH1nSHxT1+ICqN10SNT8oyo8sAUBKUD9F5EjCZM81gNinAEgrAQAAomPdunW2K/5fFaeEan4H37vEeccdd6hPd46NGTlypKppxvsgqjd+ZAkA8gjqp4ic+x3f3AGUl9x+SgsAAAAHsT179jz//PNmTjh27Nh169a98MIL8odHqfTqq6+2L/KBH1kCgAMR9VNEjqqfvrny9dwvPyAIFU9Nn3QV9VMAABBV69at00W9aBoyZIi90pmMvn379ttr4F6DBw8etteoUaPsD5lXkRIDgBP1U0TO/dRPCUdQPwUAAFGmMmTgqth1VO3jAwAij/opIof6KeEM6qcAACDKVIY8/cWnJUmOcvxr6mPy77QZU2bPm+FsjUJQPwUAV9RPETnUTwlnUD8FAABRRoZMqKB+CgCuqJ8icsgOCWdQPwUAAFFGhkyooH4KAK6onyJyyA4JZ1A/BQAAUUaGTKigfgoArqifInLIDglnUD8FAABRRoZMqKB+CgCuqJ8icsgOCWdQPwUAAFFGhkyooH4KAK6onyJyyA4JZ1A/BQAAUUaGTKigfgoArqifInLIDglnUD8FAABRRoZMqKB+CgCuqJ8icsgOCWdQPwUAAFFGhkyooH4KAK6onyJyyA4JZ1A/BQAAUUaGTKigfgoArqifInLIDglnUD8FAABRRoZMqKB+CgCuqJ8icsgOCWdQPwUAAFFGhkyooH4KAK6onyJyyA4JZ1A/BQAAUUaGTKigfgoArqifInLIDglnUD8FAABRRoZMqKB+CgCuqJ8icsgOCWdQPwUAAFFGhkyooH4KAK6onyJyyA4JZ1A/BQAAUUaGTKigfgoArqifInLIDglnUD8FAABRRoZMqKB+CgCuqJ8icsgOCWdQPwUAAFFGhkyooH4KAK6onyJyyA4JZ1A/BQAAUUaGTKigfgoArqifInLIDglnUD8FAABRRoZMqKB+CgCuqJ8icsgOCWdQPwUAAFFGhkyooH4KAK6onyJyyA4JZ1A/BQAAUUaGTKigfgoArqifInLIDglnUD8FAABRRoZMqKB+CgCuqJ8icsgOCWdQPwUAAFFGhkyooH4KAK6onyJyyA4JZ1A/BQAAUUaGTKigfgoArqifInLIDglnUD8FAABRRoZMqKB+CgCuqJ8icsgOCWdQPwUAAFFGhkyooH4KAK6onyJyyA4JZ1A/BQAAUUaGTKigfgoArqifInLIDglnUD8FAABRRoZMqKB+CgCuqJ8icsgOCWdQPwUAAFFGhkyooH4KAK6onyJyyA4JZ1A/BQAAUUaGTKigfgoArqifInLIDglnUD8FAABRRoZMqKB+CgCuqJ8icsgOCWdQPwUAAFFGhkyooH4KAK6onyJy0podbt218dKrLjKjV59Lb7j1ukcmP7jp49XO/imJydMnygOt2LTU2ZTWePzpR+Vx12x919l0wAX1UwAAEGVpzZCJAyionwKAK+qniJy0ZocffLY2K46y5crOXTjTeZPwcf0tfeT+578119mU1riqby953KVrFjibDrigfgoAAKIsrRmypKlz3nhex0uLZi1aMW/zzvXOnimJ9bkr5VHWblvubEpfrN22Qh503faVzqYDK6ifAoAr6qeInLRmh6p+2qxlk40fr1IhCdw76968tn9vWV6jVrXtX7zvvFXIeHXJnAfG35v5dI36KQAAwMEhrRlyrTo19/lYQUzhwjnn9jhn0ydrnP1DxuTpE+X+H3pstLMpfTHhqXHyoI8//aiz6cAK6qcA4Ir6KSInrdmhqp+2PK6Zs6ll6+bSZPsI6tbPNzl7purd+C3/3uBc6DO27d7k+tzMiFc/3bprY8Lbxgvv5xz4bhMG9VMAABBlac2Qa9WpeUixQx56bLSKMRNGDRs1qGmLxpJJdul2hrN/yHhl8exOXU+fOe8ZZ1P6gvopABzcqJ8ictKaHXrUT7tfeoE0TXpuovx9zgVdL7zkvOGj7ypcOKdk6ZITpz4sC9dsfVf6lC5TWrrJQumgLy3a7rST2px0nO0O25564ikd2skfw0YPrndUnTffe00t/3Dnur43X3N4pQpyPzk5OSee3OalRbP0rbqe20WWmPczYsxQ8+bjnxzboGG97OzsfPny1axdfdDdt5mdzbDVT5dvXCLPv/zh5WSh3LZKtSNvH3qrapI7kYcwn4bE0PvurN+g7ltrF+bGyrUD7rrpiCqV1XOWVTMvR+C6uVIb1E8BAECUpTVDrlWnZqnSpWwLN+9cX6NWtfz587/34TvOmxxwQf0UAA5u1E8ROWnNDuPVT7ft3tSgYT1pWvDuq/Lfpi0aV6xcMScnp/UJrWrWrv7K4tnrc1fWqlOjQMECPS/vft+4ey6/5pJC2YVkycaPV0n/PjdeLbedNf9ZfYdzF86UJdff0id33+ufbt218bg2LeW/7TudOmLM0Btuva5k6ZLyQM+/Mk3d8JgmDeWhzed242199c2fefFJeQ5yDyMfHHbv2LuPjd3VHcP+vwxqC7N+uumTNUdUqVz0kKLyVMeMv2/gkFuqVDtSWh+Z/KC0vrRolvx9yZU9zZtLxnzU0fXU32d26ywdTm7fVp7zzXfcULZc2UOKHfLqkjmq1bm5nE8mZFA/BQAAUZbWDNm1fpq79+MF6oOitw+9dfQjI56a9cTxJx571nlnqgxz7bYVN93e74R2x7ds3fzSqy5avOr/n55ktvJf29X/Jz3390+qrvzg7TeWvSJ/mO/cz1s6p8dlF0qK3qZt6/4Dr5e71U0D7rpJv+WvQtLgq6+/Qv932guTunU/W55Au9NOkhR3w473zM46nPVTWZfzLzpX0mm57Xk9u+lsfNy/HpCnZ6saj3r4Hkmt1ZW+tn6+SV4OnNbxlBbHNpVNIfm57vba2y/Jbd9au0BeMkhiPGz0YPNOUhLUTwHAFfVTRE5as0NVP23ctOE7695UIfmNZEuqPqjrqur7SsNH36VvqGqRYyaM0ksem/aIrpAuWjlf/pY8SbdeevXF+fLlW7L6jdx966eSbMnfklfpnotWzMvOzpa0Vf3Xu356wcXnyt+qaJsb+1xA/QZ1z+1xjtlfh1k/vXfs3fL3vQ8N162S4MqSM87qqP5b76g6h5Yts3XXRvXfF157TloH3TMwd+81qiS/1Lddtn5xseLFPDZXyoP6KQAAiLK0Zsjx6qdt2raWHE8SXfm7QaP6DRs3KFw4p0TJ4gUKFnhr7ULJoisdUbFQdqEOZ5x29vldDyt/WNFDiqpiorTmz5+/x2UXmvd29DFHVal2ZK7j+qeSYBcsVLDsYYdKQt7utJPkziUZVll0bixHlYc27+eUDu2Klyim/r7/0ZFyV02aHyN5+GkdTylQIH/d+rVdrzdlq5+qTz/IPUt23fHMDkWKFpEn/NxLU3P3JvmDR9yubyspd8nSJU/v0j43djGrNicdJx1atm4ut61d9+9Lxw4ccovqKfcv/21xbFNZI8mW42XpYYL6KQC4on6KyElrdqjqp64kpXtn3ZuqW9MWjSWFMt++rnxkpXIVytnurfzh5erUq6X+ljypdJnSqv649fNNkgK2Or6FajLrpye3byt/r9q8zxva3bqfLQvfWPZKbqL66XU3/f07V9Lf9n6+a5j1U3lK8od55dYV778lKaY8H/XfQXfflrX38gUSku9K2qeeZ7cLz5KmRSvmmXfepdsZ+fLlW71lWa7b5kp5UD8FAABRltYM2Vk/XZ+7csDg/pIB6m8jNWhUX/57zQ1X5cauaiX/tjnpuOzs7NmvT1cdJG+U+6lQsbxKOFuf0EpyY13KfP2dl+Xm/Qden7tv/XTFpqWFC+dIRq2ySoln5kyRHLVl6+bqv97105q1qzdpfoxuunP4AElQn5z5L7O/CrN++u6GxfIQp55+sm6V1Fpaz+zWOTf2dTHJ5Bs1OVq3PjrlIWl94tkJ8vfNd9yQZVRXJceWrFgyYZWcq/rpoWXLyMuKbbs3mR+kTVVQPwUAV9RPETlpzQ5V/bTSERVvvK2vilvuvPHu+4fozE9F0xaNyxxaWv9XMr+s2HU/j6x6hBmSMhYpUlj1UR8sVTnZpOf+Tgrvf3SkajLrp7Xq1DDvWcXtQ2/VOZl3/VQSU8kvs2IkPT3/onOfnj3Zdm86bNc/lbV4cOKoi3p1P6Hd8bIF1J20PfVE1Sr3XLBQwc5nd8rd+x77aR1PUU2SlUpP27qXKFlcFqrtZttc6QjqpwAAIMrSmiHXqlMzX758pUqXUlGseDGVKB5W/jD9nn2DRvUl9f1w5zr133c3LJYO3S48y7yfex8aLgsnT//7/fgxE0ZlGZ/37N3vyvz587+9blHuvvXTwSNul7/HPznWvB/1zTCVxHrXTyUxlif8zJwp6pv1ku7qZ2gLs3668eNVT878l/kjq3JzWeuTTjlB/feKPpdlGZ8ekEeUTaF+KLVajSqVj6xk3rP6Upck3rl766fqC2ppCuqnAOCK+ikiJ63ZYbzrn9qiaYvG5Q//59Ommz5ZI7eqWr1KnxuvtsW1/Xv/f5+PVxc9pGinrqfL353P7nRIsUPkVqrJrJ9KymXeswr12c8pzz+W61Y/veHW6/TNc2N5oeR/kq2qX6AS5/XsZrtDFWb9dOUHb1evWVWtxeld2t94W9/pL0+VLFbXTyXadzq1SJHCklBKCis99c9ANWzcoFB2Iee6S6g7t22udAT1UwAAEGVpzZBr1amZnZ199vldVZzb45xefS59YPy9+ppRubH6qSST+r9TZjwu6WKNWtUkmdTRpFmjrL1fZpesu0TJ4io33v7F+xUrH65/bdWsn3a/5Hz5W/1gqY5B9wzM2luH9a6fyp0UKJBfOpc97NCzzjvz4SfGyOOanXU4r3+64N1Xh40efFnvS+SZH1q2jLS2adtaNb329kvy3743XyN/r96yrGChgldee3lu7GdgZXmZQ0ubay0hC9uddlLu3vqpvjRBOoL6KQC4on6KyElrdhisfipRolQJM2VUseL9t/QFQ3NjX8MvUqTwex++I/+aNU2zfnpcm5b58+d//9N9EruLr+ghHdTPMUneeVj5w5yt+uenlm9copteWTxbXXRJX63fDLN+Kgml/C15sG5dn7syy/j8ae7eiz2Nffz+zmd3krxQf9/qtI6nyPIVm5aad75m67t6LZybK+VB/RQAAERZWjNk5/f3ndGgUf16R9XR/5049eGs2IVHddVVx2N734Pvfsn5hQvnbNjx3tOzJ5tVRbN+qq4TpS4IoGPkg8Oy9n43y1k/Pdmon+bGap1X9LlMViErpmLlw9XvwdrCVj/tfukF+fLly8nJkTuX53DfuHvkPnX9NDf2AYIjqx4hf9w18g65oTyK/L1u+9/58xFVKjvXesDg/rl766fyWM4nkKqgfgoArqifInLSmh0Grp926XaG3NC8mtKb771WsFDB1ie00kumvzxV+nTqerr8O+PVp/Vys35625Cb5e87hw/QrZKHlSxdskLF8uprRye0O75AwQJrty1XrVv+vaFq9Sr65s1aNpHOZvlVfb3I9XKoZv1UXaLUvJq++oKVmSb+fbGncmU7dG5folSJS6++WC8fMWZo1t7LXanYvHN99ZpVJctUn7F1bq6UB/VTAAAQZWnNkAPUT9VvjZr5YW4smdy2++8vuauY/fp06TNm/H3dup9domRx/clQs34q9yB/v7Rolnk/1/b/+4r/cxfOlL+POrpe3fq1zdbmrZro+qnkz/pul61frL62JXm72V+FWT9Vvzp1/kXn6tvK087OzjYT42GjBqnn0OLYpg0bN9APV6RIYfOKqyr0bwxQPwWA/YX6KSInrdlh4PrpopXzixUvJpnfkHvveGXxbMmKqlavUii7kPqZTh3Vavxd65R/zYVm/XTDjveqVDuyYKGCN97W98UFMx6b9ohkhFmxT32qzsNGD5b/tj6h1ZTnH5NHkada5tDS+uYPjL9X/j7+xGOfmTNFlox8cNghxQ6p36Cuqr3awqyfqjLrJVf2XLzq9UUr5t06qL8kf5Im2vI/6ZYvXz7pKeuoF364c508RP78+aVVcmVZZfVjrOpt9ly3zZXyoH4KAACiLK0ZcoD66ead6+UmlY+sZH5fvne/KyXLNT9GULtuzVM6tJMUuvulF+iFZv1UfTS1x2UXmvdcsXJFSYDV9UZbHNu0RKkS+kMAq7csK1K0iKqfrs9dKfm5eQ3Wbbs3FS6cc+LJbfQSHWb99MprL1e1Ud2qfr3A/GDE2m0rcnJy5GkXKJB/6H136uUnt29boGAB87MLU2Y8Lvkznz8FgP2L+ikiJ63ZYeD6aW7sy/Lqok5KjVrV1BVLzVC/yHnLnTeaC836aW7scvuSeOXP//elmkSVakeal8yXlPGcC7qqCzlJKnZ6l/bqHXJ98zuG3VqydEl1W+lwQrvj31n3pu1pqDDrpxs/XnXq6SerWwnJZZ+c+a/OZ3cqlF3I/MKUutiTmRyrkFT1jLM6SrKobi7p8u1Db9WtrpsrtUH9FAAARFlaM+QA9dPcve/6N2vZ5KlZT0imOmBwf8lgm7dqYr6vr34lNWvvj46qMOunEm1OOk5y2l59Ln1xwYxpL0xq2bq5tN7zwBDVKncr/+1yTqfnX5kmOfNRR9eTTFh//lRSZUmqJQOft3TO3IUz1dUARj18j/k8VZj104efGCN/d+jcfsnqN1ZsWir9ZfWzs7OPadLQvIlkv3Lnslx/M0zi5TdfyMnJObxShQcnjpLMeezj9x9atoyEZPi51E8BYP+hforISWt2GD4kx5LkbNn6xa4f+fQfa7a++9KiWUvXLHC9H9W68oO3nU25se9GSbYnHVZvWeZs9QjpLzmf+uVT11i0Yp7kfIPuvs3ZlBt7k19u/uZ7r5lXfc1MUD8FAABRltYMOVj9VOLu+4eoX14S+fPn79LtjLXbVpgdJJstWKig3L+50FY/3fTx6nN7nCPd1P1UqFjeLIBu+mRNxzM7qC9ISZ9LruzZu9+Vun66bvtKadWfSyhRsviAu24yH0uH7fqnl/W+RH8y4MiqRzwy+cHzenazfbBA/UbW6V3a2+5q+stT69SrpW4rmrZoPG/p3z9jkEv9FAD2H+qniJy0ZoeEd/S8vHvhwjm2S/jnhaB+CgAAoizPZsjbdm96Y9kr85bO2fjxKmer/9iw472X33xh0cr55hVUdbz34TsvLZplfg7Udtv5b8197e2XzGv9Jwy51atL5rj+CquKZ+ZMycrKmvTcRGeTxFtrF3p83CF9Qf0UAFxRP0Xk5Nns8OCODmec1vK4ZpIjXnldL2frfg/qpwAAIMrIkDMc2794/+QO7SpWPlxdhjXvBPVTAHBF/RSRQ3a4X6Jl6+bFihc754KuH+5c52zd70H9FAAARBkZcsZi+xfvH9emZe26NbOyskY+OMzZYf8G9VMAcEX9FJFDdkg4g/opAACIMjLkTMYpHdrVqFXN9pOweSSonwKAK+qniByyQ8IZ1E8BAECUkSETKqifAoAr6qeIHLJDwhnUTwEAQJSRIRMqqJ8CgCvqp4gcskPCGdRPAQBAlJEhEyqonwKAK+qniByyQ8IZ1E8BAECUkSETKqifAoAr6qeIHLJDwhnUTwEAQJSRIRMqqJ8CgCvqp4gcskPCGdRPAQBAlJEhEyqonwKAK+qniByyQ8IZ1E8BAECUkSETKqifAoAr6qeIHLJDwhnUTwEAQJSRIRMqqJ8CgCvqp4gcskPCGdRPAQBAlJEhEyqonwKAK+qniByyQ8IZ1E8BAECUkSETKqifAoAr6qeIHLJDwhnUTwEAQJSRIRMqqJ8CgCvqp4gcskPCGdRPAQBAlJEhEyqonwKAK+qniByyQ8IZ1E8BAECUkSETKqifAoAr6qeIHLJDwhnUTwEAQJSRIRMqqJ8CgCvqp4gcskPCGdRPAQB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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(filename='./images/GoogLeNet Inception.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b334ca060baa8706",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-06T06:36:56.388582Z",
     "start_time": "2025-08-06T06:36:43.326487Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from matplotlib_inline import backend_inline\n",
    "\n",
    "# 数据集相关\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import Dataset, DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc635996f5c76da5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-06T06:37:54.355173Z",
     "start_time": "2025-08-06T06:37:54.186756Z"
    }
   },
   "outputs": [],
   "source": [
    "transform = transforms.Compose(  # 将多个图像变换操作组合在一起\n",
    "    [\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.1307,), (0.3081,))\n",
    "    ]\n",
    ")\n",
    "\n",
    "# 下载训练集\n",
    "train_data = datasets.MNIST(\n",
    "    root='./datasets/mnist/',\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "test_data = datasets.MNIST(\n",
    "    root='./datasets/mnist/',\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "# 批次加载器\n",
    "train_loader = DataLoader(train_data, shuffle=True, batch_size=256)\n",
    "test_loader = DataLoader(test_data, shuffle=False, batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ec76d6997365a35",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-06T07:39:51.696603Z",
     "start_time": "2025-08-06T07:39:51.690597Z"
    }
   },
   "outputs": [],
   "source": [
    "class Inception(nn.Module):\n",
    "    def __init__(self, in_channels):\n",
    "        super(Inception, self).__init__()\n",
    "        self.branch1 = nn.Conv2d(in_channels=in_channels, out_channels=16, kernel_size=1)\n",
    "        self.branch2 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=in_channels, out_channels=16, kernel_size=1),\n",
    "            nn.Conv2d(in_channels=16, out_channels=24, kernel_size=3, padding=1),\n",
    "            nn.Conv2d(in_channels=24, out_channels=24, kernel_size=3, padding=1),\n",
    "        )\n",
    "        self.branch3 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=in_channels, out_channels=16, kernel_size=1),\n",
    "            nn.Conv2d(in_channels=16, out_channels=24, kernel_size=5, padding=2),\n",
    "        )\n",
    "        self.branch4 = nn.Conv2d(in_channels=in_channels, out_channels=24, kernel_size=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out1 = self.branch1(x)\n",
    "        out2 = self.branch2(x)\n",
    "        out3 = self.branch3(x)\n",
    "        out4 = self.branch4(x)\n",
    "        return torch.cat((out1, out2, out3, out4), dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a17381e68ec42fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-06T07:39:53.208768Z",
     "start_time": "2025-08-06T07:39:53.204444Z"
    }
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=1, out_channels=10, kernel_size=5), nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2),\n",
    "            Inception(in_channels=10),\n",
    "            nn.Conv2d(in_channels=88, out_channels=20, kernel_size=5), nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2),\n",
    "            Inception(in_channels=20),\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(in_features=1408, out_features=10),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.net(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ceee80123e1252bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-06T07:40:21.186307Z",
     "start_time": "2025-08-06T07:40:21.062663Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv2d output shape: \t torch.Size([1, 10, 24, 24])\n",
      "ReLU output shape: \t torch.Size([1, 10, 24, 24])\n",
      "MaxPool2d output shape: \t torch.Size([1, 10, 12, 12])\n",
      "Inception output shape: \t torch.Size([1, 88, 12, 12])\n",
      "Conv2d output shape: \t torch.Size([1, 20, 8, 8])\n",
      "ReLU output shape: \t torch.Size([1, 20, 8, 8])\n",
      "MaxPool2d output shape: \t torch.Size([1, 20, 4, 4])\n",
      "Inception output shape: \t torch.Size([1, 88, 4, 4])\n",
      "Flatten output shape: \t torch.Size([1, 1408])\n",
      "Linear output shape: \t torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "X = torch.rand(size=(1, 1, 28, 28))\n",
    "for layer in Net().net:\n",
    "    X = layer(X)\n",
    "    print(layer.__class__.__name__, 'output shape: \\t', X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "23921466dd7ed171",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = Net().to('cuda')\n",
    "\n",
    "learning_rate = 0.01\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7b6e09ec646001d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59.748138785362244\n",
      "15.049201747402549\n",
      "11.062608609907329\n",
      "9.205685402499512\n",
      "7.177769709844142\n",
      "6.679749995470047\n",
      "5.750127436127514\n",
      "5.0988464473048225\n",
      "5.13810044049751\n",
      "3.621826534741558\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = 10\n",
    "losses = []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    avg_loss = 0\n",
    "    times = 0\n",
    "\n",
    "    for x, y in train_loader:\n",
    "        x = x.to('cuda')\n",
    "        y = y.to('cuda')\n",
    "        pred = net(x)\n",
    "        loss = loss_fn(pred, y)\n",
    "        avg_loss += loss.item()\n",
    "        times += 1\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    print(avg_loss)\n",
    "    losses.append(avg_loss / times)\n",
    "\n",
    "Fig = plt.figure()\n",
    "plt.plot(range(epochs), losses)\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a6be0765843cdbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精准度: 99.0199966430664 %\n"
     ]
    }
   ],
   "source": [
    "# 测试网络\n",
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for (x, y) in test_loader:\n",
    "        # 获取小批次的x与y\n",
    "        x, y = x.to('cuda:0'), y.to('cuda:0')\n",
    "        Pred = net(x)\n",
    "        # 该局部关闭梯度计算功能\n",
    "        # 一次前向传播（小批量）\n",
    "        _, predicted = torch.max(Pred.data, dim=1)\n",
    "        correct += torch.sum((predicted == y))\n",
    "        total += y.size(0)\n",
    "\n",
    "print(f'测试集精准度: {100 * correct/total} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7abe6555-aa4d-4ed6-816a-c85c20444c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee78edc30b854065854f0004dc9abddf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(FileUpload(value=(), accept='.png,.jpg,.jpeg', description='Upload'), HBox(children=(IntSlider(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tools.ipynb_draw import DrawCanvas\n",
    "from IPython.display import display\n",
    "from ipywidgets import Button\n",
    "from PIL import Image\n",
    "\n",
    "dc = DrawCanvas(width=48, height=48, brush_size=6)\n",
    "\n",
    "callback_button = Button(description=\"识别\")\n",
    "\n",
    "def callback(_):\n",
    "    with dc.out:\n",
    "        try:\n",
    "            np_img = dc.canvas.get_image_data(width=48, height=48)\n",
    "            img = Image.fromarray(np_img)\n",
    "            transform = transforms.Compose([\n",
    "                transforms.Grayscale(),  # 如果是彩色图像转为灰度\n",
    "                transforms.Resize((28, 28)),  # 调整大小为 28 * 28\n",
    "                transforms.ToTensor(),  # 转为张量并归一化到[0,1]\n",
    "                transforms.Normalize((0.1307,), (0.3081,))  # 标准化到[-1,1]\n",
    "            ])\n",
    "\n",
    "            # 对图像进行预处理\n",
    "            input_tensor = transform(img).unsqueeze(0).to('cuda')  # 添加batch维度 (1, 1, 28, 28)\n",
    "\n",
    "            # 3. 前向传播\n",
    "            with torch.no_grad():  # 不需要计算梯度\n",
    "                output = net(input_tensor)\n",
    "                prediction = torch.argmax(output, dim=1).item()  # 获取预测类别\n",
    "                print(prediction)\n",
    "        except BaseException as e:\n",
    "            print(str(e))\n",
    "\n",
    "callback_button.on_click(callback)\n",
    "\n",
    "\n",
    "layout = dc.get_layout()\n",
    "layout.children[1].children = layout.children[1].children + (callback_button,)\n",
    "display(layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "776f61ec-61c3-4b65-b39c-077303f75d08",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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